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A k-populations algorithm for clustering categorical data
Authors:Dae-Won Kim  KiYoung Lee  Kwang H Lee
Affiliation:a Department of BioSystems and Advanced Information Technology Research Center, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu 305-701, Daejeon, Republic of Korea
b Department of Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu 305-701, Daejeon, Republic of Korea
Abstract:In this paper, the conventional k-modes-type algorithms for clustering categorical data are extended by representing the clusters of categorical data with k-populations instead of the hard-type centroids used in the conventional algorithms. Use of a population-based centroid representation makes it possible to preserve the uncertainty inherent in data sets as long as possible before actual decisions are made. The k-populations algorithm was found to give markedly better clustering results through various experiments.
Keywords:Clustering  Categorical data  Hierarchical algorithm  k-Modes algorithm  Fuzzy k-modes algorithm
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