College of Computer & Information Engineering, Henan Normal University, Xinxiang 453007, P. R. China

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Abstract—Among the large amount of genes presented in microarray gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. For this reason, reducing the dimensionality of gene expression data is imperative. Self-organizing map (SOM) is a type of mathematical cluster analysis which particularly well suited for recognizing and classifying features in complex, multidimensional data. This paper proposes an improved Self-organizing map clustering algorithm which based on neighborhood mutual information correlation measure. To evaluate the performance of the proposed approach, we apply it to six well-known gene expression datasets and compare our results with those obtained by other methods. Finally, the experimental results show that the proposed approach to gene selection is indeed efficient.

Index Terms—Self-organizing map, neighborhood mutual information, correlation measure, clustering algorithm.

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Cite: Jiucheng Xu, Tianhe Xu, Lin Sun, Jinyu Ren, "An Improved Correlation Measure-based SOM Clustering Algorithm for Gene Selection," Journal of Software vol. 8, no. 12, pp. 3082-3087, 2013.