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.
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Gan, G., Yang, Z., Wu, J. (2005). A Genetic k-Modes Algorithm for Clustering Categorical Data. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_23
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DOI: https://doi.org/10.1007/11527503_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
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