An Improved Correlation Measure-based SOM Clustering Algorithm for Gene Selection

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.