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Algorithms for Data Mining
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Author(s): Tadao Takaoka (University of Canterbury, New Zealand), Nigel K.L. Pope (Griffith University, Australia)and Kevin E. Voges (University of Canterbury, New Zealand)
Copyright: 2006
Pages: 25
Source title:
Business Applications and Computational Intelligence
Source Author(s)/Editor(s): Kevin Voges (University of Canterbury, New Zealand)and Nigel Pope (Griffith University, Australia)
DOI: 10.4018/978-1-59140-702-7.ch015
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Abstract
In this chapter, we present an overview of some common data mining algorithms. Two techniques are considered in detail. The first is association rules, a fundamental approach that is one of the oldest and most widely used techniques in data mining. It is used, for example, in supermarket basket analysis to identify relationships between purchased items. The second is the maximum sub-array problem, which is an emerging area that is yet to produce a textbook description. This area is becoming important as a new tool for data mining, particularly in the analysis of image data. For both of these techniques, algorithms are presented in pseudo-code to demonstrate the logic of the approaches. We also briefly consider decision and regression trees and clustering techniques.
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