A genetic k-modes algorithm for clustering categorical data

Authors:
Guojun Gan;Zijiang Yang;Jianhong Wu
Affiliations:
Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada;Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada;Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

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Abstract

Many optimization based clustering algorithms suffer from the possibility of stopping at locally optimal partitions of data sets. In this paper, we present a genetic k-Modes algorithm(GKMODE) that finds a globally optimal partition of a given categorical data set into a specified number of clusters. We introduce a k-Modes operator in place of the normal crossover operator. Our analysis shows that the clustering results produced by GKMODE are very high in accuracy and it performs much better than existing algorithms for clustering categorical data.