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Visual Grouping of Association Rules by Clustering Conditional Probabilities for Categorical Data

Visual Grouping of Association Rules by Clustering Conditional Probabilities for Categorical Data

Sasha Ivkovic, Ranadhir Ghosh, John Yearwood
Copyright: © 2006 |Pages: 19
ISBN13: 9781591407027|ISBN10: 1591407028|EISBN13: 9781591407041
DOI: 10.4018/978-1-59140-702-7.ch013
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MLA

Ivkovic, Sasha, et al. "Visual Grouping of Association Rules by Clustering Conditional Probabilities for Categorical Data." Business Applications and Computational Intelligence, edited by Kevin Voges and Nigel Pope, IGI Global, 2006, pp. 248-266. https://doi.org/10.4018/978-1-59140-702-7.ch013

APA

Ivkovic, S., Ghosh, R., & Yearwood, J. (2006). Visual Grouping of Association Rules by Clustering Conditional Probabilities for Categorical Data. In K. Voges & N. Pope (Eds.), Business Applications and Computational Intelligence (pp. 248-266). IGI Global. https://doi.org/10.4018/978-1-59140-702-7.ch013

Chicago

Ivkovic, Sasha, Ranadhir Ghosh, and John Yearwood. "Visual Grouping of Association Rules by Clustering Conditional Probabilities for Categorical Data." In Business Applications and Computational Intelligence, edited by Kevin Voges and Nigel Pope, 248-266. Hershey, PA: IGI Global, 2006. https://doi.org/10.4018/978-1-59140-702-7.ch013

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Abstract

We demonstrate the use of a visual data-mining tool for non-technical domain experts within organizations to facilitate the extraction of meaningful information and knowledge from in-house databases. The tool is mainly based on the basic notion of grouping association rules. Association rules are useful in discovering items that are frequently found together. However in many applications, rules with lower frequencies are often interesting for the user. Grouping of association rules is one way to overcome the rare item problem. However some groups of association rules are too large for ease of understanding. In this chapter we propose a method for clustering categorical data based on the conditional probabilities of association rules for data sets with large numbers of attributes. We argue that the proposed method provides non-technical users with a better understanding of discovered patterns in the data set.

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