Essay on Personal Philosophy of Nursing - 707 Words

Personal Philosophy of Nursing Melissa L. Fielding University Of Phoenix Personal Philosophy of Nursing A definition of a Philosophy is â€Å"when someone contemplates, or wonders, about something that serves as the blueprints or guides that incorporates each individual’s value and belief system.† (Chitty Black, 2007, p. 318) Personal Philosophy of Nursing is the core values and beliefs that a nurse upholds when taking care of another human being. It is the belief that each individual no matter what disease, race, or economic status they have will get treated with the highest regards to compassion, integrity, and respect that they deserve. I Melissa Fielding believe that my personal nursing philosophy on September 13, 2009†¦show more content†¦I will continue to involve the family members in the decision making by being sympathetic to their own sensitivities, needs, encouragement and fears. I will continue to treat my fellow colleagues with respect, knowing that they can be a help in my values and beliefs when taking care of patien ts. I will continue to take care of my own health by staying fit, eating right, and getting plenty of rest which will allow me to be the best nurse I can strive for thus being the best caregiver my patients can look forward to. In conclusion, I feel that the philosophy of nursing is a combination of core values, and beliefs that teaches us to treat each human being with the respect, compassion, dignity, and uniqueness, regardless of disease, social or economic status, or race that they deserve. My focus with each patient will be to allow them to have the right to be involved in the decision making of their care as well as allowing their family members to be involved in the decision making. I will maintain my beliefs and values by treating every patient, or coworker with respect that they deserve. I believe that each person has a calling in life and it takes a special person to become a nurse.Show MoreRelatedPersonal Nursing Philosophy : My Personal Philosophy Of Nursing1475 Words à ‚  |  6 PagesPersonal Philosophy of Nursing When one thinks of a nurse they often think of a caring, compassionate, knowledgeable individual. They don’t often think that every nurse comes from different situation, past experiences, and life changing events that make nurses who he or she is. Everyone on this earth is unique and has something to contribute. The same goes for patients. Each patient has a different background and have different interests which make them who they are. In order to give the optimalRead MorePersonal Nursing Philosophy : My Personal Philosophy Of Nursing1190 Words   |  5 PagesPersonal Philosophy of Nursing Megan A. Farrell Moberly Area Community College Introduction I, Megan Farrell, am currently a Licensed Practical Nurse at a treatment center that works with prisoners. 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When I was younger nursing was the only profession my mother pushed. It was as if everyone in our family had to be a nurse. Honestly, I think I rebelled from the thought of being a nurse simply because it was my mothers desireRead MorePersonal Philosophy of Nursing810 Words   |  4 Pages12, September 2012 Personal Philosophy of Nursing The American Nurses Association defines nursing as, â€Å"protection, and abilities, prevention of illness and injury, alleviation of suffering through the diagnosis and treatment of human response, and advocacy in the care of the individuals, families, communities, and populations.† (American Nurses Association, 2004, p. 7) There is a lot of work in nursing. There are lot of cores, focuses, visions, and philosophies of nursing. In my opinion thereRead MoreNursing Philosophy : My Personal Philosophy Of Nursing932 Words   |  4 PagesMy Philosophy of Nursing My personal philosophy of nursing began at an early age watching my mother volunteer for 25 years on the local rescue squad, following in the footsteps of her mother. I learned that helping others in a time of need should always be a priority. Respect and dignity should always be shown to people, no matter the who they are or where they are from. I have and will continue to show compassion for others while administering professional holistic care, guided by the AmericanRead MorePersonal Philosophy of Nursing1021 Words   |  5 PagesPersonal Philosophy of Nursing I believe that balance is necessary to living a healthy lifestyle. Fun and pleasure are a necessity of life. When you are living healthy, you are building up your immune system, strengthening your body and mind, fueling yourself with nutrients that will help you to grow and progress, and becoming stronger, quicker, confident, conscious, and bettering yourself all-around. Personal Philosophy on Personal Health I aim to eat as little processed foods as possibleRead MoreThe Personal Philosophy Of Nursing1642 Words   |  7 PagesThis paper is aimed at addressing the personal philosophy of nursing (PPN) in caring for the people, their-health and their-environment. PPN is defined as the way of navigating true about understanding individual or people living situation in according to their values, beliefs, health and surrounding (Whitman, Rose, 2003). This PPN has reflected many times in my previous works as an assistant in nurse, with the ACT agents known as Rubies Nursing. In this role, I have cared for both moderate and

Should Adopted Kids Have the Right to Know Who Their...

Should Adopted Kids Know Who Their Biological Parents Are? Whether adoptive children should know who their birth parents is something that is questioned too much. Children should have the right to be able to know who their birth parents are if they choose to do so. If children do not want to know who their birth parents are then they probably have a reason behind it. Children who do not know who their birth parents are, should find out who they are so that they can have contact with them. Some children are absolutely accurate that they do not want to know about who their birth parents are, but on the other hand some children do want to figure out who their birth parents are. All children that are adopted should have the right to find†¦show more content†¦This is unless that the adoptive parents make the decision to decide whether to tell the child that they are adopted. The issues of adoption can be very tender and can cause torture to the child if the information is given to them at a later age. To help take care of this situati on the information is hidden from the child until they are old enough to make their own decisions. However many children have the desire to know why they were adopted and who their biological parents are. For the child to be willing to find out who their biological parents are is something that a lot of people question. On the other hand it is the right of the child to search for their biological parents if they have the desire to. When they are grown up then they have the right to make their own decision about what they want to do. No one should have the right to tell someone not to do something that they want to do. 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The Impact Of Challenging Behaviour Education Essay Free Essays

string(63) " at hazard of attending shortage overactive upset \( ADHD \) \." The challenge for pedagogues is non to discredit or decrease the extraordinary attempts but, consistent with IDEA and the research to direct their attempts into transforming ordinary scenes so that they excessively can fit what today is regarded as extraordinary and tomorrow will be regarded as ordinary – ( Soodak et al.,2007 ) The intent of this assignment is to acknowledge the function of â€Å" Challenging Behaviour † , how it affects people when covering with their behavior and how do we assist them get bying with it by seting the theory into pattern. The appraisal of this assignment was structured on 25 hours of observation on a 13 twelvemonth old pupil during school, community and place. We will write a custom essay sample on The Impact Of Challenging Behaviour Education Essay or any similar topic only for you Order Now This assignment is sectioned into three parts which in the first portion depict what is disputing behaviors and how it affect the individual. The 2nd portion describes the pupil and his interactions with the environment around him. In the 3rd portion, this assignment describes a contemplation of what the perceiver saw during the 25 hours of observation that lead to a support program which will be built on the student`s strengths instead than concentrating on his demands. This will assist the pupil develop resilience and being able to emerge as a extremely functioning grownup. 1 ) Challenging Behaviour Terminology The nomenclature â€Å" ambitious behavior † has been used to mention to the â€Å" obstinate † or â€Å" debatable † behaviors which may be exhibited by persons with a learning disablement. There is no exact word to depict disputing behavior. Challenging behavior manifests itself into different types, changing from low to high strength. â€Å" Culturally unnatural behavior of such an strength, frequence or continuance that the physical safety of the individual or others is likely to be placed in serious hazard, or behavior which is likely to earnestly restrict usage of, or consequence in the individual being denied entree to, ordinary community installations † ( Emerson, 1995 ) . 1.1 ) The Impact of Challenging Behaviour In every behavior classified as â€Å" disputing † , there are three features in common, which: hinder the person from larning, developing and wining is harmful to the individual himself and to others puts the person at high hazard for subsequently societal jobs and school failure Persons that fall under the class of Challenging behavior frequently find themselves rejected, disliked and frequently ridiculed by the society. This group of people experience lesion in their self-esteem / assurance, accepting them to be secluded, depressed, and deprived from chances to develop, advancement and pattern societal accomplishments that they highly need. Sometimes pedagogues / society exacerbate the job. The book â€Å" Exceeding Lifes † ( 4th ed pg 133 ) , stated that excessively frequently teachers concentrate on students` shortages instead than their strengths. A concrete illustration is when persons with disputing behaviors are capable to zero tolerance policies such as suspending pupils from schools, handling them like they do non exists or when we order them to travel out of the category. This all go on when first ; the behavior is seen before the person, and 2nd ; the person in non seen as a whole individual. â€Å" Students who experience failure in one c ountry, besides tend to see failure in the other † – ( Jolivette, 2000 ) . Challenging behavior is caused by several factors interacting with each other such as environmental stressors, nerve-racking life status, kid maltreatment and school factors. â€Å" It is hence of import to step in every bit early as possible † ( Slaby, Roedell, Arezzo, and Hendrix, 1995 ) ( Tarbox, 2009 ) ( Bessell, 2001a ) 1.2 ) Covering with Challenging Behaviour To better understand when covering with disputing behavior we have to self-question: why do it go on? what intent do they ( people with C.B ) service? how can we take the job off from the individual? What actions do we take to forestall the job from happening once more? 1.3 ) Functions of Behaviour â€Å" The map of a behavior refer to the beginning of environmental support for it † – ( Tarbox et al ; 2009 ) . There are four common maps in behaviour which are: Attention: desire for attending from equals / grownups Escape: flight from individual, undertaking or environment Sensory: the behavior feels good or meets a centripetal demand Tangible: desire for a specific point or activity 1.4 ) Determining the Functioning of Behaviour To turn to disputing behaviour one demand to find its operation. Determining the maps of behavior, one demand to: Interview ( ecological event ) what type of relationship there is between the individual and his environment Direct observation ( the four maps of behavior ) Functional Analysis ( eg: S.T.A.R Model / ABC- Accident Behaviour Consequences ) Functional Hypothesis ( the information that emerges from informations / analysis ) 2 ) Student Profile Matthew is a 14 twelvemonth old adolescent. He is tall, robust and energetic. He has brown eyes and short brown hair. Matthew like custodies on activities and in fact his avocations are constructing carnival military personnels and cot, cookery, playing picture games and playing football. His future aspiration is to work in household concern and to get down one of his ain. One of his wants is to complete the secondary school every bit shortly as possible to recognize his dream. 2.1 ) Student`s Background Matthew is the eldest sibling. He has a younger brother ( Christopher ) , two old ages younger than him. Christopher attends the same secondary school as Matthew and every twenty-four hours he spends most of the clip at his grandparents` house ( few metres off from his place ) . Matthew`s parents have minimal educational cognition. Matthew`s male parent ( Joseph ) run an agricultural household concern and spend most of the twenty-four hours working in the Fieldss. Matthew`s female parent return attention of the house and when needed she besides gives her hubby a manus. 2.2 ) Student`s Medical History At the age of three Matthew was diagnosed with leukaemia. This status impaired Matthew from larning due to the fact that he ne’er went to kinder and twelvemonth one. Matthew started go toing on a regular basis to school from twelvemonth two that subsequently on he was besides found diagnosed with larning troubles ( LD ) and at hazard of attending shortage overactive upset ( ADHD ) . You read "The Impact Of Challenging Behaviour Education Essay" in category "Essay examples" 2.3 ) Educational arrangements â€Å" Childhood leukaemia subsisters may develop non verbal acquisition disablements that affect their authorship and concentration accomplishment † – ( Bessell, 2001a ) Primary schools – When go toing at the local authorities school, Matthew ( Year 2 ) found it really hard with larning. The undermentioned twelvemonth his female parent applied Matthew to be supported by a learning support helper ( Lsa ) . Harmonizing to the Statementing Moderating Panel study, Matthew was found diagnosed with larning troubles along with troubles with attending span, distractibility and impulsivity. ( These together with emotional troubles are farther lending to Matthew`s troubles in accomplishing school ) . The SMP board recommended shared support but shortly turned it into one to one support. Matthew started being supported from Year 3. Matthew repeated that same twelvemonth ( Year 3 ) because his academic public presentation was well below norm. At school Matthew started being bullied ( Year 3 -Year 5 ) . Matthew was unwilling to travel to school and frequently spliting into fits. His female parent had to alter his school because she didn`t find any cooperat ion with the school staff at that clip. Matthew attended his concluding twelvemonth of primary school at another authorities school in another vicinity. Secondary School – Matthew`s psychological study that was done in 2008 stated that he was at hazard of attending shortage overactive upset ( ADHD ) . Matthew has been go toing to this secondary school for the last three old ages, since his passage. At this school he is being supported by Inter-Disciplinary Team. 2.4 ) The Inter-Disciplinary Team Inco ( Mr Stephen Spiteri ) Head Master ( Can. Noel Saliba ) School Psychologist / Councilor ( Antonwlla Mizzi ) Lsa ( Ms. Leanne Azzopardi ) Student`s female parent ( Josette ) Student ( Matthew ) Through this squad, at school, Mathew`s academic public presentation is monitored. The Lsa in coaction with the topic instructors adapts the work for Matthew and communicates with Matthew`s female parent. The school psychologist is measuring Matthew every two months to assist him show his feelings. The head-master is the squad spokes-person / go-between. When squad members encounter troubles such as something that is impeding, different sentiments / schemes ; the caput maestro organizes a meeting to discourse these jobs. The student`s female parent helps the squad by giving and suggests utile information to the squad because she knows the most about Matthew. Mathew`s coaction with in the squad is by giving his perspective position so that squad members can accurately turn to his demands. The Inco represent the squad outside the school premises. The purpose of the squad is to see Matthew independent every bit much as possible. 2.5 ) Degree of Support Matthew is supported with a full clip Lsa ( one to one support ) . He follows the course of study with differentiated acquisition and sometimes requires disengagements during lessons such as PSD and Music. 2.6 ) Types of support Adapted press releases, visuals, head maps, colour cryptography, mold, measure by measure instructions, illustrations, motivating ( easy gets distracted ) , and ICT ( Clicker 5 used in English lessons ; synergistic boards, computing machines for composing notes ) are ever used across all topics. During appraisals ( scrutiny ) Matthew is provided with a reader, prompter and excess clip is allowed. 2.7 ) Student`s Level of Functioning Cognitive accomplishments Auditory Processing – Matthew has no job with hearing. He hears all right. The trouble is in how the encephalon interprets ( understanding the construct ) . Ocular processing – Levi does non hold any job with sight. Matthew finds trouble to organize and pull strings accurate images in his head ( scheme ) . Memory Skills – This is the country which most impairs Matthew`s acquisition. Matthew is limited to new information ( short term memory ) . He picks up merely spots and pieces of what is being said during a lesson ensuing him in doing sense of merely a small. Processing velocity – This country rely on the Memory accomplishments and there is a displacement depending on the undertaking. As stated antecedently Matthew has all right motor accomplishments and if for illustration he had to construct a cot, he performs good ( and even more rapidly than his equals ) . If he had to read / compose a short paragraph, he finds it really hard because of restrictions in more than one of the basic psychological factors. Logic and Reasoning – Matthew can execute good when categorising and grouping objects. Due to the fact that Matthew`s memory limit the information, the encephalon terminal up to treat wrong information. Communication Speech – Levi does non happen trouble in speech production. He has all right articulation, voice quality and eloquence every bit good as non verbal behaviors such as facial looks, gestures and caput and organic structure motion. Language – When giving / having information in his first linguistic communication ( Maltese ) Matthew does ticket. When he communicates in English Matthew finds it hard to pass on because of deficient vocabulary. He besides use gestures to show himself when speaking in English. Self-help accomplishments Matthew is independent in his self-help accomplishments. He has all right eye-hand coordination and finds no trouble in taking attention of himself. Socialization Matthew doesn`t find any trouble in socialising. He is a friendly individual. At school during deferral he normally likes to badger others and being ill-mannered. Matthew does non hold many friends at school. After school he spent most of the clip with his two friends. 3 ) Reflection`s Analysis Report Note: The contemplation is based on what the perceiver ( me ) saw during the 25 hours observation at school, community and place. 3.1 ) Environmental factors School Factors Through the eight hours observation at school, Matthew`s behavior was triggered by these factors: Learning ( embarrassment and ennui ) Rejection Labelled Contending Cipher have the power to command over the environment and neither Matthew has the power to command his equals, Lsa`s and instructor. Learning ( Embarrassment ) – Due to his past unwellness, cognitively, Matthew is limited to larning. Matthew feels embarrassed when he finds constructs difficult to larn. Sometimes pedagogues trigger the student`s behavior because the more they try to make their work, the more they creates jobs ( see school observation 1 A ; 8 ) . Then a clip bomb ignites with a concatenation reaction of Matthew`s temperament start escalating, Lsa start to panic because she feels defeated that her instruction was non reached, Matthew acquire worried about his self-image and get down concentrating on his equals instead on his Lsa until he explodes with the first thing he encounters. Learning ( Annoyment ) -This besides depend on how the instructor uses his resources to do the lesson interesting. There is no 1 size fits all for differentiated acquisition. As stated before, Matthew is at hazard of ADHD and if the lesson is non interesting, than the ennui triggers his attending ( see school observation 6 ) . A clear illustration of positive behavior is when lessons stimulate Matthew. In these lessons, his behavior defined as â€Å" disputing † is diminished ( see school observation 3, 4, 5,7 A ; 13 ) . These two factors have one thing in common. For these behaviors Matthew apply the â€Å" Escape † map. For him escape makes him be in a positive province. Not all behaviors occur so the individual can â€Å" obtain † something ; many behaviors occur because the individual wants to acquire off from something or avoid something wholly ( Miltenberger, 2008 ) ( Miltinberg, 2008 ) ( Cooper, 207 ) Rejection – â€Å" While it might look unusual that a individual would prosecute in a behavior to intentionally hold person scold them it can happen because for some people it ‘s better to obtain â€Å" bad † attending than no attending at all † ( Cooper, Heron A ; Heward, 2007 ) . Matthew is disliked by most of his equals and he uses inappropriate behaviors to pull attending. Attention is attracted in two ways ; either by cursing ( to affect or demoing that he is tough as show in all in the S.T.A.R theoretical account action column ) or by moving out ( observation 6 A ; seven ) . Labeling – At school Matthew is labelled. As stated antecedently sometimes instructors are the job and see merely the negative of the individual. ( See observation 11 ) . Negative labels can all excessively easy go self-fulfilling prognostications. They prevent you from seeing the kid ‘s positive qualities. They besides cause you to take down your outlooks of the person. When you can see a kid in a positive visible radiation, it helps him to see himself that manner, and to move more positively. Contending – â€Å" Alternatively they learn to anticipate rejection and may even detect that the best defence is a strong discourtesy and work stoppage out preemptively to protect themselves † – ( Moffitt, 1997 ) At school everybody knows what is Matthew`s failing and unluckily there are pupils that prefer to acquire hit and see Matthew in problem ( see school observation interruptions ) . In the yesteryear he was being bullied, and this still affects him. He uses this behavior to demo that he does non let anyone to of all time mess with him. Community A ; Home factors From the observations done in the community ( 9hours ) and at place ( 7hours ) , there is noteworthy displacement in Matthew`s behavior between that exhibited at school and that exhibited in the community and at place. In the community Matthew does non seek much attending and he is a different individual from school ( see community observation 1 A ; 3 ) . The behaviour displacements, because Matthew is non restricted by regulations and there is nil that embarrasses him such as acquisition. ( Wehmeyer, 1996 ) ( Hong, 2007 ) ( Ryan, 1995 ) When Matthew feels restricted, his behavior is triggered. At place sometimes he feels besides restricted either because he wants privateness or that when no curse is allowed ( place observation 1 ) . 3.2 ) Degree of Self-government â€Å" Self-government refers to â€Å" moving † as the primary insouciant agent in one`s life and doing picks and determinations sing one`s quality of life free from undo external influence or intervention † – ( Wehmeyer, 1996, p.24 ) To be self-determined, one has to be motivated. When there aren`t custodies on activities or stimulated feelings, Matthew loses involvement. To be motivated one has to be self-assured. Being disliked and rejected, Matthew has low self-prides and that why he uses the â€Å" flight † map because ne’er trust himself. A scheme for motive is the execution of picks. During the observations done the picks were rare and in fact, throughout the 25 hours of observation there was merely one pick given ( see school observation 2 ) . Choices aid people get motivated. â€Å" Goal scene is related to leting pupils to do picks, which besides can advance, self-government, independency, socialisation, positive behavior, and better academic public presentation † – ( Hong et al. , 2007 pg 232 ) . The demand for liberty is conceptualized in footings of sing a sense of pick, indorsement, and will with regard to initiating, maintaining, and ending behavioral battle. A If pupils are able to believe about their picks and the effects before they act, and take a safe, acceptable behavior, so the optimum result of the disciplinary procedure will hold been achieved. â€Å" To be autonomously motivated involves experiencing a sense of pick and will as a individual to the full endorses his or her ain actions or determinations † ( Ryan 1995 ) . In a nutshell, acquisition is a precedence for Matthew because it is impacting his behavior and besides his self-government. A behaviour support program will be created to assist Matthew place, control and decide inappropriate behaviors ; by being presented differentiated instruction to actuate him. Motivation helps him be self-determined and self finding increase his quality of life. 4 ) Behaviour Support program 4.1 ) Baseline When meeting acquisition that is hard to understand, lessons that do non excite him, relationships that are difficult to manage, he expresses his feelings into disputing behavior utilizing the â€Å" flight map † . This map leads him to lose the control of his behavior by ; deteriorating his relationship with his equals and instructors, restrict Matthew from larning and affects his self-pride. 4.2 ) Long Term Goal Matthew will be able learn by commanding his behavior ( choler, defeat, embarrassment and ennui ) . 4.3 ) Short-term Goals 4.31 ) Lsa A ; Teacher Lsa A ; instructors will function to pattern mature problem-solving, non fall backing to the same inappropriate behavior ( e.g: maintaining composures, Lsa communicates / talk in a low voice that merely Matthew can understand and non be heard by remainder of the category to forestall Matthew from being embarrassed and accidentally put the pupil under the limelight ; make non take the affairs personally [ panic or agitation ] and think of themselves ( pedagogues ) as fire combatants ) . Lsa A ; teacher will pull an image in the student`s head that s/he is non merely making the occupation merely to acquire paid but because s/he truly care ( indirectly inquiring the pupil for the chance to see you ( Lsa A ; teacher ) as a individual he can swear. Lsa will be prepared if the pupil fails the teacher`s illustration ( Plan B ) . Plan B consist of: Lsa will move â€Å" cool † like nil happened and still see the strength in the pupil that he can win. Lsa will get down inquiring unfastened inquiries to see what the pupil had understood. Lsa will associate the subject to the student`s avocations / life experiences by doing it more interesting instead than doing him flight ( e.g. associating Maths in mundane life state of affairs, associating English as if the pupil has to fall in love with a English adult female. Teaching schemes that motivates larning Teachers in coaction with Lsa will: Plan and portion resources to supply interesting lesson ( UDL for larning ) that stimulate â€Å" every † pupil to include everyone. Design equal coaction such as activities that involvement and prove their cognition. ( Groups consist of 5 members. Matthew`s group has to be ever arranged in a ratio of 3:1 – 3 friends and 1 equal that dislike Matthew. The negative equal ever alterations throughout the twelvemonth. This helps Matthew to go on solid his relationship with his friends and bettering his relationship with equals that dislike him. Therefore there is no demand to seek attending. Help â€Å" all † pupils change their position of cardinal to success / failure from an outside factor ( hard degree of the undertaking ) to an internal factor ( attempt, ability ) Offer picks ( in instructional scenes ) wages pupils for achieving â€Å" personal best † ends ( free clip ) Give immediate feedback. 5 ) Decision With the execution of the Behavioral Support program will assist Matthew will cut down the dispositions to the point of extinguishing them ( practising ) and interchanging them with positive behavior. Peer coaction will assist Matthew do more friends in a positive manner and there is no demand to seek attending with inappropriate behaviors. With this scheme he will larn that regard is gained by positive behavior. Choices will assist Matthew experience included and in control. It helps his self-pride and besides his development. He learns to do determinations scenes where he has some control. This aid him to larn accepting state of affairss where there is no picks to separate. He will larn to use this construct by get downing from school and go oning throughout the community. Wagess will actuate Matthew to larn and do him desire to make the undertaking once more. The more Matthew will be rewarded, the more will assist him develop relationships, addition appropriate interactions and po lish his bing accomplishments. This will assist him to develop resilience and increase his quality of life. â€Å" Ignoring the behavior on its ain is non traveling to assist ; the kid will presume they are winning or acquiring off with the behavior. Ignoring it and praising the good behavior will state the kid which behavior is appropriate † – Eileen Geiger Mentions How to cite The Impact Of Challenging Behaviour Education Essay, Essay examples

Business Law for Australian Stations Pty Ltd - myassignmenthelp

Question: Discuss about theBusiness Law for Australian Stations Pty Ltd. Answer: Introduction Annetts v Australian Station (2002) 211 CLR 317 is a leading case with regards to psychiatric injury covered under negligence and the injuries which are resulted from such tort. This particular case deals with the owed duty of care towards plaintiffs son, where the child of the plaintiff died, due to the mental harm caused to him. It was argued that due to the contravention of care on part of the defendant, the plaintiff had been injured, in form of the psychiatric injury sustained by them. When the matter was contested in the court, the claims were denied by the court to the plaintiff (Sappideen, 2009). To elucidate upon this particular case, the side of the defendant has been discussed and in addition to this, the facts of the case and the decision given in this case has been provided so that a conclusive summary of this case can be put forward. Factual Background James Annetts was the son of the plaintiff who left his home located in New South Wales in Aug 1986 and he was merely 16 (Quizlet, 2017). The reason for leaving the home was to join the work of the defendant which was in another state, i.e., in WA. The defendant had been questioned by the plaintiff before her son left NSW and the questions pertaining to the work conditions were asked. The defendant informed the plaintiff that her son would work in Flora Valley and at all time he would be properly supervised, share room with others and properly would be looked after (Federation Press, 2017). James worked at Flora Valley for 7 weeks. Despite the assurances given by the defendant, the son of the plaintiff was sent on 13th Oct, 1986 to work at a place which was 100 kms away from Flora Valley. The disappearance of the plaintiffs son was noticed by the plaintiff in 03rd Deb 1986 and he believed that James was indeed in danger or injury or even death. The plaintiff was only informed about their son being missing after three days. James father was informed over the phone regarding James having eloped from his place of work by a police officer of NSW. As soon as the father heard this news, he collapsed and the conversation was continued by James mother. Skeleton of James was discovered on 29th Apr 1987 after a lot of search. It was revealed later on that James had died as a result of exhaustion, dehydration and hypothermia on 04th Dec 1986. James had died in Gibson dessert which was quite far from the place where he was sent to work by the defendant (Federation Press, 2017). The defendant was blamed to be negligent by the plaintiff for James death. And a case for psychiatric injury caused to the plaintiff due to their sons death was initiated against the defendant by the plaintiff (Health Law Central, 2017). Arguments of Defendant The defendant clearly denies the breach of duty of care on their part and would at the very outset like to state that a duty of care was owed to James and not to the plaintiff. Hence, the very case made by the plaintiff should be rejected. In order to show that the defendant was not negligence, the very basics of this law have to be revisited in this case. Negligence stems from the breach of duty of care, which an individual owed to other (Harvey and Marston, 2009). Six separate elements have to be shown and these are obligation of care, contravention, result of contravention being loss or injury, remoteness, direct causation and lastly, foreseeability (Gibson and Fraser, 2014). For these purposes, the case of Snail in the Bottle, or as is otherwise known Donoghue v Stevenson [1932] UKHL 100 can be taken help of (Abbott, Pendlebury and Wardman, 2007). This case depicted that S, as a manufacturer, had a duty of care towards D, the consumer, as the product which was manufactured by S was consumed by D. Due to the contamination of drink as a result of presence of dead snail, the loss was foreseeable here and so, S was held liable. In the present case, the defendant in no case could have the knowledge that by listening to the news of their sons death, a psychiatric injury could be caused to him, as it was not predictable that James would die. So, the duty of care was at the most owed to James but not towards the plaintiff. The defendant would also like to highlight that the plaintiff themselves had been negligent. This can be deduced from the fact that the plaintiff had sent their son to work in an entirely new place with a stranger. Even though the defendant agrees that the plaintiff had enquired about her sons living and working arrangements and even questioned about the safety of her son. But it cannot be ignored that the defendant took adequate steps for locating the plaintiffs son, on the mere doubt that James was in danger (Australasian Legal Information Institute, 2017). Another case which has to be taken help of for showing duty of care is the case of Caparo Industries plc v Dickman [1990] 2 AC 605. In this particular case, threefold test was presented by the Court of Appeal (E-Law Resources, 2017). Three criteria had to be depicted for showing presence of duty of care, and these are, the imposed duty being fair; risk of harm being reasonably foreseeable; and proximity between parties (Lunney and Oliphant, 2013). In the matter before the court, the duty of care, as already stated was not present. This is due to the lack of direct link which the defendant and the plaintiff had. There was no foreseeability in the psychiatric injury by the defendant in a prudent way. In addition to this, there was no direct causation between the negligence of the defendant and the psychiatric injury of the plaintiff. So, in case the penalties are imposed on the defendant, they would be unfair (Australasian Legal Information Institute, 2017). With regards to the foreseeability of loss, the defendant would like to highlight the judgment of Wyong Shire Council v. Shirt (1980) 146 CLR 40. In order to show the presence of risk of harm, the judge stated in the quoted case that the view of a reasonable person had to be taken under consideration (Jade, 2017). In the matter before the court, no one, let alone the defendant, could have predicted that a worker would go away and die due to being lost in the desert. James had no purpose which required him to leave the assigned workplace. Hence, a prudent individual could not have foreseen or predicted his death. Also, neither the psychiatric injury was caused to James, to whom the defendant owed a duty of care, nor the same could have been foreseen, due to a lack of duty of care towards the plaintiff (Australasian Legal Information Institute, 2017). Deane J, in the case of Jaensch v Coffey [1984] HCA 52, highlighted the proximity of relationship, as well as, the foreseeability being present in a reasonable manner. This judge viewed that the individual would be deemed to have the capacity of predicting a certain aspect, only after contemplating the given situation. This had to be coupled with the type of relationship which attracted a legal obligation of acting in a manner which shows reasonable care, and for this, the interest of the others had to be considered. So, this case gave two cases for showing negligence, i.e., nature of relationship and the foreseeability of loss (Swarb, 2015). For considering the relationship in the present case, the sort of work which has to be handed over to an employee needs to be evaluated (Robertson and Tilbury, 2016). As has been stated by the defendant number of times, James was owed an obligation of care by the defendant, through the same was only related for the work that had been given to James. This duty was not stretched beyond the terms of the work. Hence, when James wandered off, which led to his death, the defendant could not be stated to have contravened his duties. In reality, James contravened duty of care which he owed to himself. Blaming the defendant for the same is unfair. An employer cannot predict if his employee would take off, so this loss was not foreseeable. Hence, a crucial element of foreseeability of loss was absent in case of the negligence claimed upon the defendant towards James. With regards to the plaintiff, the negligence again cannot be established as there was a lack of lawful relationship between the defendant and the plaintiff of this case. Only the deceased son was owed the obligation of care and that too based on the reasonability of the work given to him (Australasian Legal Information Institute, 2017). In order to show the presence of psychiatric injury in cases of negligence, it becomes crucial to show that the same resulted from an abrupt fright or when the same is in direct perception which takes place immediately after the incident. In the matter before the court, the disappearance news given to the plaintiff about their son was in a segmented way. Further, the news that James had died was given at a distance and even over a certain period of time. Hence, there was nothing which could be deemed as shock or immediate. Instead, the same could be stated as being agonizingly protracted. Exhaustion, along with starvation, is not things which have been witnessed by a lot of people. So, there was a differentiation in the sudden shock which the plaintiff received and between the parents who witnessed their child being mowed by a car. In the end, the defendant would like to highlight that there was an absence of foreseeability of loss sustained by James and the lack of duty of care owed towards the plaintiff (Australasian Legal Information Institute, 2017). Courts Verdict Court of Appeal of the Supreme Court of Western Australia in this particular case made a unanimous decision and rejected the plaintiffs appeal. Ipp J delivered the noteworthy ruling of this case. He stated that in order to impose an obligation of care on part of the defendant regarding the plaintiffs nervous shock, it had to be foreseeable in a reasonable manner. The plaintiff assumed a normal fortitude and the plaintiff had to exhibit the normal standards of susceptibility so that the claim made by the plaintiff pertaining to the psychiatric injury could be depicted. Along with this, an abrupt sensory perception had to be the outcome of a violation of obligation of care in a physical, as well as, temporal manner on plaintiffs part. Ipp J believed that this had to be shown for the case which could be considered as being so distressful that the psychiatric illness of recognizable nature would be suffered by the plaintiff (Allens, 2017). Stating these two points pertaining to the imposition of obligation of care, which was presented over the defendant of this case, the appeal was denied by the Court of Appeals. The court opined that in the matter of normal fortitude, it could never be foreseeable in a reasonable manner, and so, a claim for a psychiatric injury drawing from the same could not be upheld, merely because the plaintiff had lost their child. A recognized psychiatric injury had to be differentiated from loss of child, which is an ordinary incident and they both fell under different kind of loss. The plaintiff could not show before the court that between the plaintiff and the defendant, there was a certain amount of physical proximity, in time and space sense. Further, the court also held that due to the psychiatric injury taking place far away from where James had died, there was an absence of obligation of care. The court advised the plaintiff to accept that their son was no more and the same was not the d efendants fault. While concluding the case, the court rejected the appeal made by the plaintiff and the defendant was not held negligent (Allens, 2017). References Abbott, K., Pendlebury, N., and Wardman, K. (2007) Business Law. 8th ed. London: Thomson. Allens. (2017) 2001 Annual Review of Insurance Law - Duty of Care, General Tortious and Trade Practices Act Liability. [Online] Allens. Available from: https://www.allens.com.au/pubs/ari/2001/care.htm [Accessed on: 25/05/17] Australasian Legal Information Institute. (2017) Tame v New South Wales [2002] HCA 35; 211 CLR 317; 191 ALR 449; 76 ALJR 1348 (5 September 2002). [Online] Australasian Legal Information Institute. Available from: https://www.austlii.edu.au/cgi-bin/sinodisp/au/cases/cth/HCA/2002/35.html?stem=0synonyms=0query=Annetts%20v%20Australian%20Station [Accessed on: 25/05/17] E-Law Resources. (2017) Caparo Industries PLC v Dickman [1990] 2 AC 605 House of Lords. [Online] E-Law Resources. Available from: https://www.healthlawcentral.com/cases/tame-v-new-south-wales/ [Accessed on: 25/05/17] Federation Press. (2017) Tame v New South Wales Annetts v Australian Stations Pty Ltd. [Online] Federation Press. Available from: https://www.federationpress.com.au/pdf/Tame%20v%20New%20South%20Wales.pdf [Accessed on: 25/05/17] Gibson, A., and Fraser, D. (2014) Business Law 2014. 8th ed. Melbourne: Pearson Education Australia. Harvey, B., and Marston, J. (2009) Cases and Commentary on Tort. 6th ed. New York: Oxford University Press. Health Law Central. (2017) Tame v New South Wales; Annetts v Australian Stations Pty Limited [2002] HCA 35. [Online] Health Law Central. Available from: https://www.healthlawcentral.com/cases/tame-v-new-south-wales/ [Accessed on: 25/05/17] Jade. (2017) Wyong Shire Council v Shirt. [Online] Jade. Available from: https://jade.io/article/66842 [Accessed on: 25/05/17] Latimer, P. (2012) Australian Business Law 2012. 31st ed. Sydney, NSW: CCH Australia Limited. Lunney, M., and Oliphant, K. (2013) Tort Law: Text and Materials. 5th ed. Oxford: Oxford University Press. Quizlet. (2017) Torts B Lecture #1--Pure Psychiatric Harm. [Online] Quizlet. Available from: https://quizlet.com/45679268/torts-b-lecture-1-pure-psychiatric-harm-flash-cards/ [Accessed on: 25/05/17] Robertson, A., and Tilbury, M. (2016) Divergences in Private Law. Oxford: Hart Publishing. Sappideen, C., at al. (2009) Torts, Commentary and Materials. 10th ed. Pyrmont: Lawbook Co, pp. 255-63. Swarb. (2015) Jaensch v Coffey; 20 Aug 1984. [Online] Swarb. Available from: https://swarb.co.uk/jaensch-v-coffey-20-aug-1984/ [Accessed on: 25/05/17]

Mr.Rodgers Essays - Fred Rogers, Mister Rogers, Fred Rogers

He basically saved public television. In 1969 the government wanted to cut public television funds. Mister Rogers then went to Washington where he gave an amazing merely six minute speech. By the end of the speech not only did he charm the hostile Senators, he got them to double the budget they would have initially cut down. The whole thing can be found on youtube, a video called ?Mister Rogers defending PBS to the US Senate.? ?Certain fundamentalist preachers hated him because, apparently not getting the ?kindest man who ever lived? memo, they would ask him to denounce homosexuals. Mr. Rogers?s response? He?d pat the target on the shoulder and say, ?God loves you just as you are.? Rogers even belonged to a ?More Light? congregation in Pittsburgh, a part of the Presbyterian Church dedicated to welcoming LGBT persons to full participation in the church.? According to a TV Guide piece on him, Fred Rogers drove a plain old Impala for years. One day, however, the car was stolen from the street near the TV station. When Rogers filed a police report, the story was picked up by every newspaper, radio and media outlet around town. Amazingly, within 48 hours the car was left in the exact spot where it was taken from, with an apology on the dashboard. It read, ?If we?d known it was yours, we never would have taken it.? Once, on a fancy trip up to a PBS exec?s house, he heard the limo driver was going to wait outside for 2 hours, so he insisted the driver come in and join them (which flustered the host). On the way back, Rogers sat up front, and when he learned that they were passing the driver?s home on the way, he asked if they could stop in to meet his family. According to the driver, it was one of the best nights of his life?the house supposedly lit up when Rogers arrived, and he played jazz piano and bantered with them late into the night. Further, like with the reporters, Rogers sent him notes and kept in touch with the driver for the rest of his life.

How to Solve Marketing Fire Drills with Kyle DeWeerdt from Apprenda [PODCAST]

How to Solve Marketing Fire Drills with Kyle DeWeerdt from Apprenda [PODCAST] Marketing fire drills: Can you learn to take care of them before they turn into bona fide emergencies? It can be stressful and overwhelming when projects crop up with little to no notice. Planning where you can and having good communication with your team can help you get through it with no negative ramifications. Today’s guest is Kyle DeWeerdt, marketing programs manager at Apprenda. He has come up with a simple system to help his team prioritize their time to complete their work, nipping stressful emergencies in the bud. He’s going to help us learn how to resolve issues before they even start. Some of the topics you’ll hear about today include: Some information about Apprenda and the types of content that Kyle works with, as well as Kyle’s background. An explanation of â€Å"marketing fire drills†: What are they, and what can you do about them? An explanation of buffer time, and how it can help you handle these emergencies that come up. How to break down a project to define a deadline and a publish date for content. How Kyle manages the process behind the scenes with multiple teams to make sure every task is completed on time. Kyle’s best tips for marketers who want to manage their projects more efficiently.

A Good Leader Essay Example | Topics and Well Written Essays - 500 words

A Good Leader - Essay Example I am required to plan and organize events and ensure the harmony of group members, is a great experience for future positions in the business world where it might be necessary for me to organize corporate gatherings and facilitate contract negotiations. I will defend it whether it is right or wrong. I entirely disagree. I perceive myself as a malleable person and I also believe I consider other’s opinions. If I defend my opinion, it means that I am confident about its validity. I believe that if I am stubborn about my ideas it is in regards to my ambition and desire to see projects through to their full potential. However, I realize that candidness towards other people’s ideas is very important in business. Without being open to other people’s ideas and perspectives, it is impossible to successfully collaborate. In business, effective collaboration is built on the open trust and freedom of expression of all group members. Through this open environment, the group is then able to compare ideas and attain a goal that would be impossible the sole efforts of an individual. Even as I ultimately see myself as a leader, I think it’s important to consider Franklin Roosevelt who said, â€Å"A good leader can't get too far ahead of his followers.† While I hope to function as a strong beacon of direction for my friends, I realize that it’s important to not forget the essential similarity of all human-kind and that the great thing about having friends is the chance to share the great journey of life with someone that understands.

Analyze how innovation, design, and creativity at Mcdonalds support Research Paper

Analyze how innovation, design, and creativity at Mcdonalds support the organizations goals and objectives - Research Paper Example When it comes to its design of leadership, McDonalds top management has ensured that they are customer oriented and hence has engaged in corporate social responsibility to work hand on hand with its customers who are part of the larger community in order to fulfill its values and objectives. This step has increased the fame of the food stores and even increased its customer base. McDonalds have also invested in having different designs of their worker’s uniforms depending on the occasion or where they are serving their customers, this range from entertaining children in their numerous playgrounds and even serving customers in their dinners. Their creativity is evident from their logo which is unique and identifies it wherever it is. They are also creative in their advertisements and in the advertisements they sponsor. McDonald’s different designs of their restaurants including drive-in ones that serve the customer needs wherever they are is also an indication of their creativity and which goes a long way to fulfill the goal of McDonalds of serving fast food to all people and at their own

Understanding Geospatial Data in Development Assignment

Understanding Geospatial Data in Development - Assignment Example A band ratio approach can be used by diving band 5 by band 2 in order to separate the water line from the clouds. The rate of change of the coastline can be calculated for transects greater than 16000 and generated at intervals of 50 m along the coastline and the main islands. This can be done using the End point Rate technique in the Digital Shoreline Analysis System in ArcGIS. Bangladesh is located at the mouth of Brahmaputra and Ganges which are the two largest rivers in the world flowing from the Himalayas. A large part of the country is located in the Bengal basin which is an extensive geosyncline and has a large population of about 14.2 million people. Most people live in the low lying plains floodplains and delta plains which are usually very vulnerable to flooding during the monsoon season (Alesheikh et al, 2007). As a result, Bangladesh is normally considered as one of the most risky countries in the world due to exposure to the effects of climate change and sea level rises. The coastline of Bangladesh covers an area of about 47,201square kilometers and this region is inhabited by about 46 million people. River Ganges drains about 1114000 square kilometers of catchment area and the River Brahmaputra drains 935000 square kilometers of catchment area and these supplies billions of tonnes of sediments every year in the Bengal basin. This rapid increas e in sedimentation results into a very rapid accretion in the estuaries (Goodbred, 2003). In other sections of the coastline where rapid erosion is experienced due to strong tidal currents and strong waves action, rapid subsidence can be noted with a recession of about 3-4 km of the shoreline from its original position. If we compare the Landsat images between 1973 and 2000, the recession rate of the shoreline and the time frame can be established (Benny, 2000). By comparing the satellite images

Identifying Clusters in High Dimensional Data

Identifying Clusters in High Dimensional Data â€Å"Ask those who remember, are mindful if you do not know).† (Holy Quran, 6:43) Removal Of Redundant Dimensions To Find Clusters In N-Dimensional Data Using Subspace Clustering Abstract The data mining has emerged as a powerful tool to extract knowledge from huge databases. Researchers have introduced several machine learning algorithms to explore the databases to discover information, hidden patterns, and rules from the data which were not known at the data recording time. Due to the remarkable developments in the storage capacities, processing and powerful algorithmic tools, practitioners are developing new and improved algorithms and techniques in several areas of data mining to discover the rules and relationship among the attributes in simple and complex higher dimensional databases. Furthermore data mining has its implementation in large variety of areas ranging from banking to marketing, engineering to bioinformatics and from investment to risk analysis and fraud detection. Practitioners are analyzing and implementing the techniques of artificial neural networks for classification and regression problems because of accuracy, efficiency. The aim of his short r esearch project is to develop a way of identifying the clusters in high dimensional data as well as redundant dimensions which can create a noise in identifying the clusters in high dimensional data. Techniques used in this project utilizes the strength of the projections of the data points along the dimensions to identify the intensity of projection along each dimension in order to find cluster and redundant dimension in high dimensional data. 1 Introduction In numerous scientific settings, engineering processes, and business applications ranging from experimental sensor data and process control data to telecommunication traffic observation and financial transaction monitoring, huge amounts of high-dimensional measurement data are produced and stored. Whereas sensor equipments as well as big storage devices are getting cheaper day by day, data analysis tools and techniques wrap behind. Clustering methods are common solutions to unsupervised learning problems where neither any expert knowledge nor some helpful annotation for the data is available. In general, clustering groups the data objects in a way that similar objects get together in clusters whereas objects from different clusters are of high dissimilarity. However it is observed that clustering disclose almost no structure even it is known there must be groups of similar objects. In many cases, the reason is that the cluster structure is stimulated by some subsets of the spaces dim ensions only, and the many additional dimensions contribute nothing other than making noise in the data that hinder the discovery of the clusters within that data. As a solution to this problem, clustering algorithms are applied to the relevant subspaces only. Immediately, the new question is how to determine the relevant subspaces among the dimensions of the full space. Being faced with the power set of the set of dimensions a brute force trial of all subsets is infeasible due to their exponential number with respect to the original dimensionality. In high dimensional data, as dimensions are increasing, the visualization and representation of the data becomes more difficult and sometimes increase in the dimensions can create a bottleneck. More dimensions mean more visualization or representation problems in the data. As the dimensions are increased, the data within those dimensions seems dispersing towards the corners / dimensions. Subspace clustering solves this problem by identifying both problems in parallel. It solves the problem of relevant subspaces which can be marked as redundant in high dimensional data. It also solves the problem of finding the cluster structures within that dataset which become apparent in these subspaces. Subspace clustering is an extension to the traditional clustering which automatically finds the clusters present in the subspace of high dimensional data space that allows better clustering the data points than the original space and it works even when the curse of dimensionality occurs. The most o f the clustering algorithms have been designed to discover clusters in full dimensional space so they are not effective in identifying the clusters that exists within subspace of the original data space. The most of the clustering algorithms produces clustering results based on the order in which the input records were processed [2]. Subspace clustering can identify the different cluster within subspaces which exists in the huge amount of sales data and through it we can find which of the different attributes are related. This can be useful in promoting the sales and in planning the inventory levels of different products. It can be used for finding the subspace clusters in spatial databases and some useful decisions can be taken based on the subspace clusters identified [2]. The technique used here for indentifying the redundant dimensions which are creating noise in the data in order to identifying the clusters consist of drawing or plotting the data points in all dimensions. At second step the projection of all data points along each dimension are plotted. At the third step the unions of projections along each dimension are plotted using all possible combinations among all no. of dimensions and finally the union of all projection along all dimensions and analyzed, it will show the contribution of each dimension in indentifying the cluster which will be represented by the weight of projection. If any of the given dimension is contributing very less in order to building the weight of projection, that dimension can be considered as redundant, which means this dimension is not so important to identify the clusters in given data. The details of this strategy will be covered in later chapters. 2 Data Mining 2.1 What is Data Mining? Data mining is the process of analyzing data from different perspective and summarizing it for getting useful information. The information can be used for many useful purposes like increasing revenue, cuts costs etc. The data mining process also finds the hidden knowledge and relationship within the data which was not known while data recording. Describing the data is the first step in data mining, followed by summarizing its attributes (like standard deviation mean etc). After that data is reviewed using visual tools like charts and graphs and then meaningful relations are determined. In the data mining process, the steps of collecting, exploring and selecting the right data are critically important. User can analyze data from different dimensions categorize and summarize it. Data mining finds the correlation or patterns amongst the fields in large databases. Data mining has a great potential to help companies to focus on their important information in their data warehouse. It can predict the future trends and behaviors and allows the business to make more proactive and knowledge driven decisions. It can answer the business questions that were traditionally much time consuming to resolve. It scours databases for hidden patterns for finding predictive information that experts may miss it might lies beyond their expectations. Data mining is normally used to transform the data into information or knowledge. It is commonly used in wide range of profiting practices such as marketing, fraud detection and scientific discovery. Many companies already collect and refine their data. Data mining techniques can be implemented on existing platforms for enhance the value of information resources. Data mining tools can analyze massive databases to deliver answers to the questions. Some other terms contains similar meaning from data mining such as â€Å"Knowledge mining† or â€Å"Knowledge Extraction† or â€Å"Pattern Analysis†. Data mining can also be treated as a Knowledge Discovery from Data (KDD). Some people simply mean the data mining as an essential step in Knowledge discovery from a large data. The process of knowledge discovery from data contains following steps. * Data cleaning (removing the noise and inconsistent data) * Data Integration (combining multiple data sources) * Data selection (retrieving the data relevant to analysis task from database) * Data Transformation (transforming the data into appropriate forms for mining by performing summary or aggregation operations) * Data mining (applying the intelligent methods in order to extract data patterns) * Pattern evaluation (identifying the truly interesting patterns representing knowledge based on some measures) * Knowledge representation (representing knowledge techniques that are used to present the mined knowledge to the user) 2.2 Data Data can be any type of facts, or text, or image or number which can be processed by computer. Todays organizations are accumulating large and growing amounts of data in different formats and in different databases. It can include operational or transactional data which includes costs, sales, inventory, payroll and accounting. It can also include nonoperational data such as industry sales and forecast data. It can also include the meta data which is, data about the data itself, such as logical database design and data dictionary definitions. 2.3 Information The information can be retrieved from the data via patterns, associations or relationship may exist in the data. For example the retail point of sale transaction data can be analyzed to yield information about the products which are being sold and when. 2.4 Knowledge Knowledge can be retrieved from information via historical patterns and the future trends. For example the analysis on retail supermarket sales data in promotional efforts point of view can provide the knowledge buying behavior of customer. Hence items which are at most risk for promotional efforts can be determined by manufacturer easily. 2.5 Data warehouse The advancement in data capture, processing power, data transmission and storage technologies are enabling the industry to integrate their various databases into data warehouse. The process of centralizing and retrieving the data is called data warehousing. Data warehousing is new term but concept is a bit old. Data warehouse is storage of massive amount of data in electronic form. Data warehousing is used to represent an ideal way of maintaining a central repository for all organizational data. Purpose of data warehouse is to maximize the user access and analysis. The data from different data sources are extracted, transformed and then loaded into data warehouse. Users / clients can generate different types of reports and can do business analysis by accessing the data warehouse. Data mining is primarily used today by companies with a strong consumer focus retail, financial, communication, and marketing organizations. It allows these organizations to evaluate associations between certain internal external factors. The product positioning, price or staff skills can be example of internal factors. The external factor examples can be economic indicators, customer demographics and competition. It also allows them to calculate the impact on sales, corporate profits and customer satisfaction. Furthermore it allows them to summarize the information to look detailed transactional data. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by its capabilities. Data mining usually automates the procedure of searching predictive information in huge databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data very quickly. The targeted marketing can be an example of predictive problem. Data mining utilizes data on previous promotional mailings in order to recognize the targets most probably to increase return on investment as maximum as possible in future mailings. Tools used in data mining traverses through huge databases and discover previously unseen patterns in single step. Analysis on retail sales data to recognize apparently unrelated products which are usually purchased together can be an example of it. The more pattern discovery problems can include identifying fraudulent credit card transactions and identifying irregular data that could symbolize data entry input errors. When data mining tools are used on parallel processing systems of high performance, they are able to analy ze huge databases in very less amount of time. Faster or quick processing means that users can automatically experience with more details to recognize the complex data. High speed and quick response makes it actually possible for users to examine huge amounts of data. Huge databases, in turn, give improved and better predictions. 2.6 Descriptive and Predictive Data Mining Descriptive data mining aims to find patterns in the data that provide some information about what the data contains. It describes patterns in existing data, and is generally used to create meaningful subgroups such as demographic clusters. For example descriptions are in the form of Summaries and visualization, Clustering and Link Analysis. Predictive Data Mining is used to forecast explicit values, based on patterns determined from known results. For example, in the database having records of clients who have already answered to a specific offer, a model can be made that predicts which prospects are most probable to answer to the same offer. It is usually applied to recognize data mining projects with the goal to identify a statistical or neural network model or set of models that can be used to predict some response of interest. For example, a credit card company may want to engage in predictive data mining, to derive a (trained) model or set of models that can quickly identify tr ansactions which have a high probability of being fraudulent. Other types of data mining projects may be more exploratory in nature (e.g. to determine the cluster or divisions of customers), in which case drill-down descriptive and tentative methods need to be applied. Predictive data mining is goad oriented. It can be decomposed into following major tasks. * Data Preparation * Data Reduction * Data Modeling and Prediction * Case and Solution Analysis 2.7 Text Mining The Text Mining is sometimes also called Text Data Mining which is more or less equal to Text Analytics. Text mining is the process of extracting/deriving high quality information from the text. High quality information is typically derived from deriving the patterns and trends through means such as statistical pattern learning. It usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. The High Quality in text mining usually refers to some combination of relevance, novelty, and interestingness. The text categorization, concept/entity extraction, text clustering, sentiment analysis, production of rough taxonomies, entity relation modeling, document summarization can be included as text mining tasks. Text Mining is also known as the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Linking together of the extracted information is the key element to create new facts or new hypotheses to be examined further by more conventional ways of experimentation. In text mining, the goal is to discover unknown information, something that no one yet knows and so could not have yet written down. The difference between ordinary data mining and text mining is that, in text mining the patterns are retrieved from natural language text instead of from structured databases of facts. Databases are designed and developed for programs to execute automatically; text is written for people to read. Most of the researchers think that it will need a full fledge simulation of how the brain works before that programs that read the way people do could be written. 2.8 Web Mining Web Mining is the technique which is used to extract and discover the information from web documents and services automatically. The interest of various research communities, tremendous growth of information resources on Web and recent interest in e-commerce has made this area of research very huge. Web mining can be usually decomposed into subtasks. * Resource finding: fetching intended web documents. * Information selection and pre-processing: selecting and preprocessing specific information from fetched web resources automatically. * Generalization: automatically discovers general patterns at individual and across multiple website * Analysis: validation and explanation of mined patterns. Web Mining can be mainly categorized into three areas of interest based on which part of Web needs to be mined: Web Content Mining, Web Structure Mining and Web Usage Mining. Web Contents Mining describes the discovery of useful information from the web contents, data and documents [10]. In past the internet consisted of only different types of services and data resources. But today most of the data is available over the internet; even digital libraries are also available on Web. The web contents consist of several types of data including text, image, audio, video, metadata as well as hyperlinks. Most of the companies are trying to transform their business and services into electronic form and putting it on Web. As a result, the databases of the companies which were previously residing on legacy systems are now accessible over the Web. Thus the employees, business partners and even end clients are able to access the companys databases over the Web. Users are accessing the application s over the web via their web interfaces due to which the most of the companies are trying to transform their business over the web, because internet is capable of making connection to any other computer anywhere in the world [11]. Some of the web contents are hidden and hence cannot be indexed. The dynamically generated data from the results of queries residing in the database or private data can fall in this area. Unstructured data such as free text or semi structured data such as HTML and fully structured data such as data in the tables or database generated web pages can be considered in this category. However unstructured text is mostly found in the web contents. The work on Web content mining is mostly done from 2 point of views, one is IR and other is DB point of view. â€Å"From IR view, web content mining assists and improves the information finding or filtering to the user. From DB view web content mining models the data on the web and integrates them so that the more soph isticated queries other than keywords could be performed. [10]. In Web Structure Mining, we are more concerned with the structure of hyperlinks within the web itself which can be called as inter document structure [10]. It is closely related to the web usage mining [14]. Pattern detection and graphs mining are essentially related to the web structure mining. Link analysis technique can be used to determine the patterns in the graph. The search engines like Google usually uses the web structure mining. For example, the links are mined and one can then determine the web pages that point to a particular web page. When a string is searched, a webpage having most number of links pointed to it may become first in the list. Thats why web pages are listed based on rank which is calculated by the rank of web pages pointed to it [14]. Based on web structural data, web structure mining can be divided into two categories. The first kind of web structure mining interacts with extracting patterns from the hyperlinks in the web. A hyperlink is a structural comp onent that links or connects the web page to a different web page or different location. The other kind of the web structure mining interacts with the document structure, which is using the tree-like structure to analyze and describe the HTML or XML tags within the web pages. With continuous growth of e-commerce, web services and web applications, the volume of clickstream and user data collected by web based organizations in their daily operations has increased. The organizations can analyze such data to determine the life time value of clients, design cross marketing strategies etc. [13]. The Web usage mining interacts with data generated by users clickstream. â€Å"The web usage data includes web server access logs, proxy server logs, browser logs, user profile, registration data, user sessions, transactions, cookies, user queries, bookmark data, mouse clicks and scrolls and any other data as a result of interaction† [10]. So the web usage mining is the most important task of the web mining [12]. Weblog databases can provide rich information about the web dynamics. In web usage mining, web log records are mined to discover the user access patterns through which the potential customers can be identified, quality of internet services can be enhanc ed and web server performance can be improved. Many techniques can be developed for implementation of web usage mining but it is important to know that success of such applications depends upon what and how much valid and reliable knowledge can be discovered the log data. Most often, the web logs are cleaned, condensed and transformed before extraction of any useful and significant information from weblog. Web mining can be performed on web log records to find associations patterns, sequential patterns and trend of web accessing. The overall Web usage mining process can be divided into three inter-dependent stages: data collection and pre-processing, pattern discovery, and pattern analysis [13]. In the data collection preprocessing stage, the raw data is collected, cleaned and transformed into a set of user transactions which represents the activities of each user during visits to the web site. In the pattern discovery stage, statistical, database, and machine learning operations a re performed to retrieve hidden patterns representing the typical behavior of users, as well as summary of statistics on Web resources, sessions, and users. 3 Classification 3.1 What is Classification? As the quantity and the variety increases in the available data, it needs some robust, efficient and versatile data categorization technique for exploration [16]. Classification is a method of categorizing class labels to patterns. It is actually a data mining methodology used to predict group membership for data instances. For example, one may want to use classification to guess whether the weather on a specific day would be â€Å"sunny†, â€Å"cloudy† or â€Å"rainy†. The data mining techniques which are used to differentiate similar kind of data objects / points from other are called clustering. It actually uses attribute values found in the data of one class to distinguish it from other types or classes. The data classification majorly concerns with the treatment of the large datasets. In classification we build a model by analyzing the existing data, describing the characteristics of various classes of data. We can use this model to predict the class/type of new data. Classification is a supervised machine learning procedure in which individual items are placed in a group based on quantitative information on one or more characteristics in the items. Decision Trees and Bayesian Networks are the examples of classification methods. One type of classification is Clustering. This is process of finding the similar data objects / points within the given dataset. This similarity can be in the meaning of distance measures or on any other parameter, depending upon the need and the given data. Classification is an ancient term as well as a modern one since classification of animals, plants and other physical objects is still valid today. Classification is a way of thinking about things rather than a study of things itself so it draws its theory and application from complete range of human experiences and thoughts [18]. From a bigger picture, classification can include medical patients based on disease, a set of images containing red rose from an image database, a set of documents describing â€Å"classification† from a document/text database, equipment malfunction based on cause and loan applicants based on their likelihood of payment etc. For example in later case, the problem is to predict a new applicants loans eligibility given old data about customers. There are many techniques which are used for data categorization / classification. The most common are Decision tree classifier and Bayesian classifiers. 3.2 Types of Classification There are two types of classification. One is supervised classification and other is unsupervised classification. Supervised learning is a machine learning technique for discovering a function from training data. The training data contains the pairs of input objects, and their desired outputs. The output of the function can be a continuous value which can be called regression, or can predict a class label of the input object which can be called as classification. The task of the supervised learner is to predict the value of the function for any valid input object after having seen a number of training examples (i.e. pairs of input and target output). To achieve this goal, the learner needs to simplify from the presented data to hidden situations in a meaningful way. The unsupervised learning is a class of problems in machine learning in which it is needed to seek to determine how the data are organized. It is distinguished from supervised learning in that the learner is given only unknown examples. Unsupervised learning is nearly related to the problem of density estimation in statistics. However unsupervised learning also covers many other techniques that are used to summarize and explain key features of the data. One form of unsupervised learning is clustering which will be covered in next chapter. Blind source partition based on Independent Component Analysis is another example. Neural network models, adaptive resonance theory and the self organizing maps are most commonly used unsupervised learning algorithms. There are many techniques for the implementation of supervised classification. We will be discussing two of them which are most commonly used which are Decision Trees classifiers and Naà ¯ve Bayesian Classifiers. 3.2.1 Decision Trees Classifier There are many alternatives to represent classifiers. The decision tree is probably the most widely used approach for this purpose. It is one of the most widely used supervised learning methods used for data exploration. It is easy to use and can be represented in if-then-else statements/rules and can work well in noisy data as well [16]. Tree like graph or decisions models and their possible consequences including resource costs, chance event, outcomes, and utilities are used in decision trees. Decision trees are most commonly used in specifically in decision analysis, operations research, to help in identifying a strategy most probably to reach a target. In machine learning and data mining, a decision trees are used as predictive model; means a planning from observations calculations about an item to the conclusions about its target value. More descriptive names for such tree models are classification tree or regression tree. In these tree structures, leaves are representing class ifications and branches are representing conjunctions of features those lead to classifications. The machine learning technique for inducing a decision tree from data is called decision tree learning, or decision trees. Decision trees are simple but powerful form of multiple variable analyses [15]. Classification is done by tree like structures that have different test criteria for a variable at each of the nodes. New leaves are generated based on the results of the tests at the nodes. Decision Tree is a supervised learning system in which classification rules are constructed from the decision tree. Decision trees are produced by algorithms which identify various ways splitting data set into branch like segment. Decision tree try to find out a strong relationship between input and target values within the dataset [15]. In tasks classification, decision trees normally visualize that what steps should be taken to reach on classification. Every decision tree starts with a parent node called root node which is considered to be the parent of every other node. Each node in the tree calculates an attribute in the data and decides which path it should follow. Typically the decision test is comparison of a value against some constant. Classification with the help of decision tree is done by traversing from the root node up to a leaf node. Decision trees are able to represent and classify the diverse types of data. The simplest form of data is numerical data which is most familiar too. Organizing nominal data is also required many times in many situations. Nominal quantities are normally represented via discrete set of symbols. For example weather condition can be described in either nominal fashion or numeric. Quantification can be done about temperature by saying that it is eleven degrees Celsius or fifty two degrees Fahrenheit. The cool, mild, cold, warm or hot terminologies can also be sued. The former is a type of numeric data while and the latter is an example of nominal data. More precisely, the example of cool, mild, cold, warm and hot is a special type of nominal data, expressed as ordinal data. Ordinal data usually has an implicit assumption of ordered relationships among the values. In the weather example, purely nominal description like rainy, overcast and sunny can also be added. These values have no relationships or distance measures among each other. Decision Trees are those types of trees where each node is a question, each branch is an answer to a question, and each leaf is a result. Here is an example of Decision tree. Roughly, the idea is based upon the number of stock items; we have to make different decisions. If we dont have much, you buy at any cost. If you have a lot of items then you only buy if it is inexpensive. Now if stock items are less than 10 then buy all if unit price is less than 10 otherwise buy only 10 items. Now if we have 10 to 40 items in the stock then check unit price. If unit price is less than 5 £ then buy only 5 items otherwise no need to buy anything expensive since stock is good already. Now if we have more than 40 items in the stock, then buy 5 if and only if price is less than 2 £ otherwise no need to buy too expensive items. So in this way decision trees help us to make a decision at each level. Here is another example of decision tree, representing the risk factor associated with the rash driving. The root node at the top of the tree structure is showing the feature that is split first for highest discrimination. The internal nodes are showing decision rules on one or more attributes while leaf nodes are class labels. A person having age less than 20 has very high risk while a person having age greater than 30 has a very low risk. A middle category; a person having age greater than 20 but less than 30 depend upon another attribute which is car type. If car type is of sports then there is again high risk involved while if family car is used then there is low risk involved. In the field of sciences engineering and in the applied areas including business intelligence and data mining, many useful features are being introduced as the result of evolution of decision trees. * With the help of transformation in decision trees, the volume of data can be reduced into more compact form that preserves the major characteristic Identifying Clusters in High Dimensional Data Identifying Clusters in High Dimensional Data â€Å"Ask those who remember, are mindful if you do not know).† (Holy Quran, 6:43) Removal Of Redundant Dimensions To Find Clusters In N-Dimensional Data Using Subspace Clustering Abstract The data mining has emerged as a powerful tool to extract knowledge from huge databases. Researchers have introduced several machine learning algorithms to explore the databases to discover information, hidden patterns, and rules from the data which were not known at the data recording time. Due to the remarkable developments in the storage capacities, processing and powerful algorithmic tools, practitioners are developing new and improved algorithms and techniques in several areas of data mining to discover the rules and relationship among the attributes in simple and complex higher dimensional databases. Furthermore data mining has its implementation in large variety of areas ranging from banking to marketing, engineering to bioinformatics and from investment to risk analysis and fraud detection. Practitioners are analyzing and implementing the techniques of artificial neural networks for classification and regression problems because of accuracy, efficiency. The aim of his short r esearch project is to develop a way of identifying the clusters in high dimensional data as well as redundant dimensions which can create a noise in identifying the clusters in high dimensional data. Techniques used in this project utilizes the strength of the projections of the data points along the dimensions to identify the intensity of projection along each dimension in order to find cluster and redundant dimension in high dimensional data. 1 Introduction In numerous scientific settings, engineering processes, and business applications ranging from experimental sensor data and process control data to telecommunication traffic observation and financial transaction monitoring, huge amounts of high-dimensional measurement data are produced and stored. Whereas sensor equipments as well as big storage devices are getting cheaper day by day, data analysis tools and techniques wrap behind. Clustering methods are common solutions to unsupervised learning problems where neither any expert knowledge nor some helpful annotation for the data is available. In general, clustering groups the data objects in a way that similar objects get together in clusters whereas objects from different clusters are of high dissimilarity. However it is observed that clustering disclose almost no structure even it is known there must be groups of similar objects. In many cases, the reason is that the cluster structure is stimulated by some subsets of the spaces dim ensions only, and the many additional dimensions contribute nothing other than making noise in the data that hinder the discovery of the clusters within that data. As a solution to this problem, clustering algorithms are applied to the relevant subspaces only. Immediately, the new question is how to determine the relevant subspaces among the dimensions of the full space. Being faced with the power set of the set of dimensions a brute force trial of all subsets is infeasible due to their exponential number with respect to the original dimensionality. In high dimensional data, as dimensions are increasing, the visualization and representation of the data becomes more difficult and sometimes increase in the dimensions can create a bottleneck. More dimensions mean more visualization or representation problems in the data. As the dimensions are increased, the data within those dimensions seems dispersing towards the corners / dimensions. Subspace clustering solves this problem by identifying both problems in parallel. It solves the problem of relevant subspaces which can be marked as redundant in high dimensional data. It also solves the problem of finding the cluster structures within that dataset which become apparent in these subspaces. Subspace clustering is an extension to the traditional clustering which automatically finds the clusters present in the subspace of high dimensional data space that allows better clustering the data points than the original space and it works even when the curse of dimensionality occurs. The most o f the clustering algorithms have been designed to discover clusters in full dimensional space so they are not effective in identifying the clusters that exists within subspace of the original data space. The most of the clustering algorithms produces clustering results based on the order in which the input records were processed [2]. Subspace clustering can identify the different cluster within subspaces which exists in the huge amount of sales data and through it we can find which of the different attributes are related. This can be useful in promoting the sales and in planning the inventory levels of different products. It can be used for finding the subspace clusters in spatial databases and some useful decisions can be taken based on the subspace clusters identified [2]. The technique used here for indentifying the redundant dimensions which are creating noise in the data in order to identifying the clusters consist of drawing or plotting the data points in all dimensions. At second step the projection of all data points along each dimension are plotted. At the third step the unions of projections along each dimension are plotted using all possible combinations among all no. of dimensions and finally the union of all projection along all dimensions and analyzed, it will show the contribution of each dimension in indentifying the cluster which will be represented by the weight of projection. If any of the given dimension is contributing very less in order to building the weight of projection, that dimension can be considered as redundant, which means this dimension is not so important to identify the clusters in given data. The details of this strategy will be covered in later chapters. 2 Data Mining 2.1 What is Data Mining? Data mining is the process of analyzing data from different perspective and summarizing it for getting useful information. The information can be used for many useful purposes like increasing revenue, cuts costs etc. The data mining process also finds the hidden knowledge and relationship within the data which was not known while data recording. Describing the data is the first step in data mining, followed by summarizing its attributes (like standard deviation mean etc). After that data is reviewed using visual tools like charts and graphs and then meaningful relations are determined. In the data mining process, the steps of collecting, exploring and selecting the right data are critically important. User can analyze data from different dimensions categorize and summarize it. Data mining finds the correlation or patterns amongst the fields in large databases. Data mining has a great potential to help companies to focus on their important information in their data warehouse. It can predict the future trends and behaviors and allows the business to make more proactive and knowledge driven decisions. It can answer the business questions that were traditionally much time consuming to resolve. It scours databases for hidden patterns for finding predictive information that experts may miss it might lies beyond their expectations. Data mining is normally used to transform the data into information or knowledge. It is commonly used in wide range of profiting practices such as marketing, fraud detection and scientific discovery. Many companies already collect and refine their data. Data mining techniques can be implemented on existing platforms for enhance the value of information resources. Data mining tools can analyze massive databases to deliver answers to the questions. Some other terms contains similar meaning from data mining such as â€Å"Knowledge mining† or â€Å"Knowledge Extraction† or â€Å"Pattern Analysis†. Data mining can also be treated as a Knowledge Discovery from Data (KDD). Some people simply mean the data mining as an essential step in Knowledge discovery from a large data. The process of knowledge discovery from data contains following steps. * Data cleaning (removing the noise and inconsistent data) * Data Integration (combining multiple data sources) * Data selection (retrieving the data relevant to analysis task from database) * Data Transformation (transforming the data into appropriate forms for mining by performing summary or aggregation operations) * Data mining (applying the intelligent methods in order to extract data patterns) * Pattern evaluation (identifying the truly interesting patterns representing knowledge based on some measures) * Knowledge representation (representing knowledge techniques that are used to present the mined knowledge to the user) 2.2 Data Data can be any type of facts, or text, or image or number which can be processed by computer. Todays organizations are accumulating large and growing amounts of data in different formats and in different databases. It can include operational or transactional data which includes costs, sales, inventory, payroll and accounting. It can also include nonoperational data such as industry sales and forecast data. It can also include the meta data which is, data about the data itself, such as logical database design and data dictionary definitions. 2.3 Information The information can be retrieved from the data via patterns, associations or relationship may exist in the data. For example the retail point of sale transaction data can be analyzed to yield information about the products which are being sold and when. 2.4 Knowledge Knowledge can be retrieved from information via historical patterns and the future trends. For example the analysis on retail supermarket sales data in promotional efforts point of view can provide the knowledge buying behavior of customer. Hence items which are at most risk for promotional efforts can be determined by manufacturer easily. 2.5 Data warehouse The advancement in data capture, processing power, data transmission and storage technologies are enabling the industry to integrate their various databases into data warehouse. The process of centralizing and retrieving the data is called data warehousing. Data warehousing is new term but concept is a bit old. Data warehouse is storage of massive amount of data in electronic form. Data warehousing is used to represent an ideal way of maintaining a central repository for all organizational data. Purpose of data warehouse is to maximize the user access and analysis. The data from different data sources are extracted, transformed and then loaded into data warehouse. Users / clients can generate different types of reports and can do business analysis by accessing the data warehouse. Data mining is primarily used today by companies with a strong consumer focus retail, financial, communication, and marketing organizations. It allows these organizations to evaluate associations between certain internal external factors. The product positioning, price or staff skills can be example of internal factors. The external factor examples can be economic indicators, customer demographics and competition. It also allows them to calculate the impact on sales, corporate profits and customer satisfaction. Furthermore it allows them to summarize the information to look detailed transactional data. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by its capabilities. Data mining usually automates the procedure of searching predictive information in huge databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data very quickly. The targeted marketing can be an example of predictive problem. Data mining utilizes data on previous promotional mailings in order to recognize the targets most probably to increase return on investment as maximum as possible in future mailings. Tools used in data mining traverses through huge databases and discover previously unseen patterns in single step. Analysis on retail sales data to recognize apparently unrelated products which are usually purchased together can be an example of it. The more pattern discovery problems can include identifying fraudulent credit card transactions and identifying irregular data that could symbolize data entry input errors. When data mining tools are used on parallel processing systems of high performance, they are able to analy ze huge databases in very less amount of time. Faster or quick processing means that users can automatically experience with more details to recognize the complex data. High speed and quick response makes it actually possible for users to examine huge amounts of data. Huge databases, in turn, give improved and better predictions. 2.6 Descriptive and Predictive Data Mining Descriptive data mining aims to find patterns in the data that provide some information about what the data contains. It describes patterns in existing data, and is generally used to create meaningful subgroups such as demographic clusters. For example descriptions are in the form of Summaries and visualization, Clustering and Link Analysis. Predictive Data Mining is used to forecast explicit values, based on patterns determined from known results. For example, in the database having records of clients who have already answered to a specific offer, a model can be made that predicts which prospects are most probable to answer to the same offer. It is usually applied to recognize data mining projects with the goal to identify a statistical or neural network model or set of models that can be used to predict some response of interest. For example, a credit card company may want to engage in predictive data mining, to derive a (trained) model or set of models that can quickly identify tr ansactions which have a high probability of being fraudulent. Other types of data mining projects may be more exploratory in nature (e.g. to determine the cluster or divisions of customers), in which case drill-down descriptive and tentative methods need to be applied. Predictive data mining is goad oriented. It can be decomposed into following major tasks. * Data Preparation * Data Reduction * Data Modeling and Prediction * Case and Solution Analysis 2.7 Text Mining The Text Mining is sometimes also called Text Data Mining which is more or less equal to Text Analytics. Text mining is the process of extracting/deriving high quality information from the text. High quality information is typically derived from deriving the patterns and trends through means such as statistical pattern learning. It usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. The High Quality in text mining usually refers to some combination of relevance, novelty, and interestingness. The text categorization, concept/entity extraction, text clustering, sentiment analysis, production of rough taxonomies, entity relation modeling, document summarization can be included as text mining tasks. Text Mining is also known as the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Linking together of the extracted information is the key element to create new facts or new hypotheses to be examined further by more conventional ways of experimentation. In text mining, the goal is to discover unknown information, something that no one yet knows and so could not have yet written down. The difference between ordinary data mining and text mining is that, in text mining the patterns are retrieved from natural language text instead of from structured databases of facts. Databases are designed and developed for programs to execute automatically; text is written for people to read. Most of the researchers think that it will need a full fledge simulation of how the brain works before that programs that read the way people do could be written. 2.8 Web Mining Web Mining is the technique which is used to extract and discover the information from web documents and services automatically. The interest of various research communities, tremendous growth of information resources on Web and recent interest in e-commerce has made this area of research very huge. Web mining can be usually decomposed into subtasks. * Resource finding: fetching intended web documents. * Information selection and pre-processing: selecting and preprocessing specific information from fetched web resources automatically. * Generalization: automatically discovers general patterns at individual and across multiple website * Analysis: validation and explanation of mined patterns. Web Mining can be mainly categorized into three areas of interest based on which part of Web needs to be mined: Web Content Mining, Web Structure Mining and Web Usage Mining. Web Contents Mining describes the discovery of useful information from the web contents, data and documents [10]. In past the internet consisted of only different types of services and data resources. But today most of the data is available over the internet; even digital libraries are also available on Web. The web contents consist of several types of data including text, image, audio, video, metadata as well as hyperlinks. Most of the companies are trying to transform their business and services into electronic form and putting it on Web. As a result, the databases of the companies which were previously residing on legacy systems are now accessible over the Web. Thus the employees, business partners and even end clients are able to access the companys databases over the Web. Users are accessing the application s over the web via their web interfaces due to which the most of the companies are trying to transform their business over the web, because internet is capable of making connection to any other computer anywhere in the world [11]. Some of the web contents are hidden and hence cannot be indexed. The dynamically generated data from the results of queries residing in the database or private data can fall in this area. Unstructured data such as free text or semi structured data such as HTML and fully structured data such as data in the tables or database generated web pages can be considered in this category. However unstructured text is mostly found in the web contents. The work on Web content mining is mostly done from 2 point of views, one is IR and other is DB point of view. â€Å"From IR view, web content mining assists and improves the information finding or filtering to the user. From DB view web content mining models the data on the web and integrates them so that the more soph isticated queries other than keywords could be performed. [10]. In Web Structure Mining, we are more concerned with the structure of hyperlinks within the web itself which can be called as inter document structure [10]. It is closely related to the web usage mining [14]. Pattern detection and graphs mining are essentially related to the web structure mining. Link analysis technique can be used to determine the patterns in the graph. The search engines like Google usually uses the web structure mining. For example, the links are mined and one can then determine the web pages that point to a particular web page. When a string is searched, a webpage having most number of links pointed to it may become first in the list. Thats why web pages are listed based on rank which is calculated by the rank of web pages pointed to it [14]. Based on web structural data, web structure mining can be divided into two categories. The first kind of web structure mining interacts with extracting patterns from the hyperlinks in the web. A hyperlink is a structural comp onent that links or connects the web page to a different web page or different location. The other kind of the web structure mining interacts with the document structure, which is using the tree-like structure to analyze and describe the HTML or XML tags within the web pages. With continuous growth of e-commerce, web services and web applications, the volume of clickstream and user data collected by web based organizations in their daily operations has increased. The organizations can analyze such data to determine the life time value of clients, design cross marketing strategies etc. [13]. The Web usage mining interacts with data generated by users clickstream. â€Å"The web usage data includes web server access logs, proxy server logs, browser logs, user profile, registration data, user sessions, transactions, cookies, user queries, bookmark data, mouse clicks and scrolls and any other data as a result of interaction† [10]. So the web usage mining is the most important task of the web mining [12]. Weblog databases can provide rich information about the web dynamics. In web usage mining, web log records are mined to discover the user access patterns through which the potential customers can be identified, quality of internet services can be enhanc ed and web server performance can be improved. Many techniques can be developed for implementation of web usage mining but it is important to know that success of such applications depends upon what and how much valid and reliable knowledge can be discovered the log data. Most often, the web logs are cleaned, condensed and transformed before extraction of any useful and significant information from weblog. Web mining can be performed on web log records to find associations patterns, sequential patterns and trend of web accessing. The overall Web usage mining process can be divided into three inter-dependent stages: data collection and pre-processing, pattern discovery, and pattern analysis [13]. In the data collection preprocessing stage, the raw data is collected, cleaned and transformed into a set of user transactions which represents the activities of each user during visits to the web site. In the pattern discovery stage, statistical, database, and machine learning operations a re performed to retrieve hidden patterns representing the typical behavior of users, as well as summary of statistics on Web resources, sessions, and users. 3 Classification 3.1 What is Classification? As the quantity and the variety increases in the available data, it needs some robust, efficient and versatile data categorization technique for exploration [16]. Classification is a method of categorizing class labels to patterns. It is actually a data mining methodology used to predict group membership for data instances. For example, one may want to use classification to guess whether the weather on a specific day would be â€Å"sunny†, â€Å"cloudy† or â€Å"rainy†. The data mining techniques which are used to differentiate similar kind of data objects / points from other are called clustering. It actually uses attribute values found in the data of one class to distinguish it from other types or classes. The data classification majorly concerns with the treatment of the large datasets. In classification we build a model by analyzing the existing data, describing the characteristics of various classes of data. We can use this model to predict the class/type of new data. Classification is a supervised machine learning procedure in which individual items are placed in a group based on quantitative information on one or more characteristics in the items. Decision Trees and Bayesian Networks are the examples of classification methods. One type of classification is Clustering. This is process of finding the similar data objects / points within the given dataset. This similarity can be in the meaning of distance measures or on any other parameter, depending upon the need and the given data. Classification is an ancient term as well as a modern one since classification of animals, plants and other physical objects is still valid today. Classification is a way of thinking about things rather than a study of things itself so it draws its theory and application from complete range of human experiences and thoughts [18]. From a bigger picture, classification can include medical patients based on disease, a set of images containing red rose from an image database, a set of documents describing â€Å"classification† from a document/text database, equipment malfunction based on cause and loan applicants based on their likelihood of payment etc. For example in later case, the problem is to predict a new applicants loans eligibility given old data about customers. There are many techniques which are used for data categorization / classification. The most common are Decision tree classifier and Bayesian classifiers. 3.2 Types of Classification There are two types of classification. One is supervised classification and other is unsupervised classification. Supervised learning is a machine learning technique for discovering a function from training data. The training data contains the pairs of input objects, and their desired outputs. The output of the function can be a continuous value which can be called regression, or can predict a class label of the input object which can be called as classification. The task of the supervised learner is to predict the value of the function for any valid input object after having seen a number of training examples (i.e. pairs of input and target output). To achieve this goal, the learner needs to simplify from the presented data to hidden situations in a meaningful way. The unsupervised learning is a class of problems in machine learning in which it is needed to seek to determine how the data are organized. It is distinguished from supervised learning in that the learner is given only unknown examples. Unsupervised learning is nearly related to the problem of density estimation in statistics. However unsupervised learning also covers many other techniques that are used to summarize and explain key features of the data. One form of unsupervised learning is clustering which will be covered in next chapter. Blind source partition based on Independent Component Analysis is another example. Neural network models, adaptive resonance theory and the self organizing maps are most commonly used unsupervised learning algorithms. There are many techniques for the implementation of supervised classification. We will be discussing two of them which are most commonly used which are Decision Trees classifiers and Naà ¯ve Bayesian Classifiers. 3.2.1 Decision Trees Classifier There are many alternatives to represent classifiers. The decision tree is probably the most widely used approach for this purpose. It is one of the most widely used supervised learning methods used for data exploration. It is easy to use and can be represented in if-then-else statements/rules and can work well in noisy data as well [16]. Tree like graph or decisions models and their possible consequences including resource costs, chance event, outcomes, and utilities are used in decision trees. Decision trees are most commonly used in specifically in decision analysis, operations research, to help in identifying a strategy most probably to reach a target. In machine learning and data mining, a decision trees are used as predictive model; means a planning from observations calculations about an item to the conclusions about its target value. More descriptive names for such tree models are classification tree or regression tree. In these tree structures, leaves are representing class ifications and branches are representing conjunctions of features those lead to classifications. The machine learning technique for inducing a decision tree from data is called decision tree learning, or decision trees. Decision trees are simple but powerful form of multiple variable analyses [15]. Classification is done by tree like structures that have different test criteria for a variable at each of the nodes. New leaves are generated based on the results of the tests at the nodes. Decision Tree is a supervised learning system in which classification rules are constructed from the decision tree. Decision trees are produced by algorithms which identify various ways splitting data set into branch like segment. Decision tree try to find out a strong relationship between input and target values within the dataset [15]. In tasks classification, decision trees normally visualize that what steps should be taken to reach on classification. Every decision tree starts with a parent node called root node which is considered to be the parent of every other node. Each node in the tree calculates an attribute in the data and decides which path it should follow. Typically the decision test is comparison of a value against some constant. Classification with the help of decision tree is done by traversing from the root node up to a leaf node. Decision trees are able to represent and classify the diverse types of data. The simplest form of data is numerical data which is most familiar too. Organizing nominal data is also required many times in many situations. Nominal quantities are normally represented via discrete set of symbols. For example weather condition can be described in either nominal fashion or numeric. Quantification can be done about temperature by saying that it is eleven degrees Celsius or fifty two degrees Fahrenheit. The cool, mild, cold, warm or hot terminologies can also be sued. The former is a type of numeric data while and the latter is an example of nominal data. More precisely, the example of cool, mild, cold, warm and hot is a special type of nominal data, expressed as ordinal data. Ordinal data usually has an implicit assumption of ordered relationships among the values. In the weather example, purely nominal description like rainy, overcast and sunny can also be added. These values have no relationships or distance measures among each other. Decision Trees are those types of trees where each node is a question, each branch is an answer to a question, and each leaf is a result. Here is an example of Decision tree. Roughly, the idea is based upon the number of stock items; we have to make different decisions. If we dont have much, you buy at any cost. If you have a lot of items then you only buy if it is inexpensive. Now if stock items are less than 10 then buy all if unit price is less than 10 otherwise buy only 10 items. Now if we have 10 to 40 items in the stock then check unit price. If unit price is less than 5 £ then buy only 5 items otherwise no need to buy anything expensive since stock is good already. Now if we have more than 40 items in the stock, then buy 5 if and only if price is less than 2 £ otherwise no need to buy too expensive items. So in this way decision trees help us to make a decision at each level. Here is another example of decision tree, representing the risk factor associated with the rash driving. The root node at the top of the tree structure is showing the feature that is split first for highest discrimination. The internal nodes are showing decision rules on one or more attributes while leaf nodes are class labels. A person having age less than 20 has very high risk while a person having age greater than 30 has a very low risk. A middle category; a person having age greater than 20 but less than 30 depend upon another attribute which is car type. If car type is of sports then there is again high risk involved while if family car is used then there is low risk involved. In the field of sciences engineering and in the applied areas including business intelligence and data mining, many useful features are being introduced as the result of evolution of decision trees. * With the help of transformation in decision trees, the volume of data can be reduced into more compact form that preserves the major characteristic