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

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Author(s): Sasha Ivkovic (University of Ballarat, Australia), Ranadhir Ghosh (University of Ballarat, Australia)and John Yearwood (University of Ballarat, Australia)
Copyright: 2006
Pages: 19
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.ch013

Keywords: Business & Management / Business Information Systems / Business Intelligence / Business Science Reference

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

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