Learning Patterns from Clusters Using Reduct

Authors:
Alka Arora;Shuchita Upadhyaya;Rajni Jain
Affiliations:
Indian Agricultural Statistics Research Institute, New Delhi, India 110012;Department of Computer Science and Application, Kurukshetra University, Kurukshetra, India;National Centre for Agricultural Economics and Policy Research Pusa Campus, New Delhi, India 110012
Venue:
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Year:
2008

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Abstract

Usual clustering algorithms just generate general description of the clusters like which entities are member of each cluster and lacks in generating cluster description in the form of pattern. Pattern is defined as a logical statement describing a cluster structure in terms of relevant attributes. In the proposed approach reduct from rough set theory is employed to generate pattern. Reduct is defined as the set of attributes which distinguishes the entities in a homogenous cluster, therefore these can be clear cut removed from the same. Remaining attributes are ranked for their contribution in the cluster. Cluster description is then formed by conjunction of most contributing attributes. Proposed approach is demonstrated using benchmarking mushroom dataset from UCI repository.