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Variations on Associative Classifiers and Classification Results Analyses

Variations on Associative Classifiers and Classification Results Analyses

Maria-Luiza Antonie, David Chodos, Osmar Zaïane
Copyright: © 2009 |Pages: 23
ISBN13: 9781605664040|ISBN10: 1605664049|ISBN13 Softcover: 9781616925963|EISBN13: 9781605664057
DOI: 10.4018/978-1-60566-404-0.ch009
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MLA

Antonie, Maria-Luiza, et al. "Variations on Associative Classifiers and Classification Results Analyses." Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, edited by Yanchang Zhao, et al., IGI Global, 2009, pp. 150-172. https://doi.org/10.4018/978-1-60566-404-0.ch009

APA

Antonie, M., Chodos, D., & Zaïane, O. (2009). Variations on Associative Classifiers and Classification Results Analyses. In Y. Zhao, C. Zhang, & L. Cao (Eds.), Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction (pp. 150-172). IGI Global. https://doi.org/10.4018/978-1-60566-404-0.ch009

Chicago

Antonie, Maria-Luiza, David Chodos, and Osmar Zaïane. "Variations on Associative Classifiers and Classification Results Analyses." In Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, edited by Yanchang Zhao, Chengqi Zhang, and Longbing Cao, 150-172. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-404-0.ch009

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Abstract

The chapter introduces the associative classifier, a classification model based on association rules, and describes the three phases of the model building process: rule generation, pruning, and selection. In the first part of the chapter, these phases are described in detail, and several variations on the associative classifier model are presented within the context of the relevant phase. These variations are: mining data sets with re-occurring items, using negative association rules, and pruning rules using graph-based techniques. Each of these departs from the standard model in a crucial way, and thus expands the classification potential. The second part of the chapter describes a system, ARC-UI that allows a user to analyze the results of classifying an item using an associative classifier. This system uses an intuitive, Web-based interface and, with this system, the user is able to see the rules that were used to classify an item, modify either the item being classified or the rule set that was used, view the relationship between attributes, rules and classes in the rule set, and analyze the training data set with respect to the item being classified.

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