The automated identification and description of clusters for categorical data using conditional probabilities
- Authors: Ivkovic, Sasha , Yearwood, John , Ghosh, Ranadhir
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at CIMCA 2004: International Conference on Computational Intelligence for Modelling, Control & Automation, Gold Coast, Queensland : 12th July, 2004
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000885
Visual grouping of association rules by clustering conditional probabilities for categorical data
- Authors: Ivkovic, Sasha , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Book chapter
- Relation: Business Applications and Computational Intelligence p. 248-266
- Full Text: false
- Reviewed:
- Description: 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.