A genetic K-means clustering algorithm applied to gene expression data

Authors:
Fang-Xiang Wu;W. J. Zhang;Anthony J. Kusalik
Affiliations:
Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada;Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada;Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
Venue:
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Year:
2003

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Abstract

One of the current main strategies to understand a biological process at genome level is to cluster genes by their expression data obtained from DNA microarray experiments. The classic K-means clustering algorithm is a deterministic search and may terminate in a locally optimal clustering. In this paper, a genetic K-means clustering algorithm, called GKMCA, for clustering in gene expression datasets is described. GKMCA is a hybridization of a genetic algorithm (GA) and the iterative optimal K-means algorithm (IOKMA). In GKMCA, each individual is encoded by a partition table which uniquely determines a clustering, and three genetic operators (selection, crossover, mutation) and an IOKM operator derived from IOKMA are employed. The superiority of the GKMCA over the IOKMA and over other GA-clustering algorithms without the IOKM operator is demonstrated for two real gene expression datasets.