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