FGKA: a Fast Genetic K-means Clustering Algorithm

Authors:
Yi Lu;Shiyong Lu;Farshad Fotouhi;Youping Deng;Susan J. Brown
Affiliations:
Wayne State University, Detroit, MI;Wayne State University, Detroit, MI;Wayne State University, Detroit, MI;Kansas State University, Manhattan, KS;Kansas State University, Manhattan, KS
Venue:
Proceedings of the 2004 ACM symposium on Applied computing
Year:
2004

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Abstract

In this paper, we propose a new clustering algorithm called Fast Genetic K-means Algorithm (FGKA). FGKA is inspired by the Genetic K-means Algorithm (GKA) proposed by Krishna and Murty in 1999 but features several improvements over GKA. Our experiments indicate that, while K-means algorithm might converge to a local optimum, both FGKA and GKA always converge to the global optimum eventually but FGKA runs much faster than GKA.