Kernel k'-means algorithm for clustering analysis

Authors:
Yue Zhao;Shuyi Zhang;Jinwen Ma
Affiliations:
Department of Information Science, School of Mathematical Sciences And LMAM, Peking University, Beijing, China;Department of Information Science, School of Mathematical Sciences And LMAM, Peking University, Beijing, China;Department of Information Science, School of Mathematical Sciences And LMAM, Peking University, Beijing, China
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Year:
2013

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Abstract

k'-means algorithm is a new improvement of k-means algorithm. It implements a rewarding and penalizing competitive learning mechanism into the k-means paradigm such that the number of clusters can be automatically determined for a given dataset. This paper further proposes the kernelized versions of k'-means algorithms with four different discrepancy metrics. It is demonstrated by the experiments on both synthetic and real-world datasets that these kernel k'-means algorithms can automatically detect the number of actual clusters in a dataset, with a classification accuracy rate being considerably better than those of the corresponding k'-means algorithms.