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Data Mining – A First View
Roiger & Geatz
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Definition
Data mining is the process of employing one or more computer learning techniques to automatically analyze and extract knowledge contained within a database.Knowledge Discovery in Databases (KDD) is same a data mining.Knowledge from a data mining session gives us a model or generalization of the data.Induction-based learning – generalize by observing specifics.
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What Can Computer Learn?
FactsConceptsProceduresPrinciplesComputers are good at learning concepts – concepts are the outputs from a data mining session.
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Three Concept Views
Classical view – all concepts have definite defining properties.Probabilistic view – concepts are represented by properties that are probable of concept members.Exemplar view –a given instance is determined to be example of a particular concept if the instance is similar enough to set of one or more known examples of that concept.
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Supervised Learning
Also known as induction-based supervised concept learningAttribute-value matrix – table 1.1Decision tree
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Unsupervised Clustering
Builds models without predefined classes.Table 1.3.Example questions.
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Data Mining?
Can we clearly define the problem?Does potentially meaningful data exist?Does the data contain hidden knowledge? Or is the data factual and useful for reporting purposes only?
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Data Mining or Data Query
Shallow knowledge – factual, easily stored and manipulated. SQL is a good tool.Multidimensional knowledge – is also factual but multidimensional knowledge _ OLAP tools.Hidden knowledge – patterns and regularities in data – no SQL – data mining algorithms.Deep knowledge – knowledge in database that can be found only with some direction – current data mining tools are ineffective.
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Expert Systems or Data Mining
Data Mining: Data – data mining tool – knowledge
Expert Systems – Human Expert – Knowledge Engineer – ES building tool – Knowledge
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Data Mining Application
Fraud detectionHealth careBusiness and financeScientific applicationsSports and gaming