Genetic weighted k-means algorithm for clustering large-scale gene expression dataReport as inadecuate

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BMC Bioinformatics

, 9:S12

First Online: 28 May 2008


BackgroundThe traditional unweighted k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It is sensitive to initial partitions, its result is prone to the local minima, and it is only applicable to data with spherical-shape clusters. The last shortcoming means that we must assume that gene expression data at the different conditions follow the independent distribution with the same variances. Nevertheless, this assumption is not true in practice.

ResultsIn this paper, we propose a genetic weighted K-means algorithm denoted by GWKMA, which solves the first two problems and partially remedies the third one. GWKMA is a hybridization of a genetic algorithm GA and a weighted K-means algorithm WKMA. In GWKMA, each individual is encoded by a partitioning table which uniquely determines a clustering, and three genetic operators selection, crossover, mutation and a WKM operator derived from WKMA are employed. The superiority of the GWKMA over the k-means is illustrated on a synthetic and two real-life gene expression datasets.

ConclusionThe proposed algorithm has general application to clustering large-scale biological data such as gene expression data and peptide mass spectral data.

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Author: Fang-Xiang Wu



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