AN IMPROVED UNSUPERVISED CLUSTERING ALGORITHM BASED ON POPULATION MARKOV CHAIN

F.W. Yang, H.J. Lin, and S.H. Yen

Keywords

Unsupervised clustering, genetic algorithms, population Markov chain, cluster validity, DaviesBouldin index

Abstract

GA-based clustering approaches have the advantage of automatically determining the optimal number of clusters. In a previous work, we proposed an efficient GA-based clustering algorithm, the PMCC method, and compared it with a representative GA-based clustering algorithm, the GCUK method, to prove its efficiency and effectiveness. In this paper we modify this PMCC method to obtain an improved version: the WPMCC method. This modification prevents premature convergence problem caused in the PMCC method while maintaining the advantage of the PMCC method. The experimental results show that the proposed algorithm not only solves the problem of premature convergence, thereby providing a more stable clustering performance in terms of number of clusters and clustering results, but it also improves the efficiency in terms of time.

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