A Greedy Correlation Measure Based Attribute Clustering Algorithm for Gene Selection

This paper proposes an attribute clustering algorithm for grouping attributes into clusters so as to obtain meaningful modes from microarray data. First the problem of attribute clustering is analyzed and neighborhood mutual information is introduced to solve it. Furthermore, an attribute clustering algorithm is presented for grouping attributes into clusters through optimizing a criterion function which is derived from an information measure that reflects the correlation between attributes. Then, by applying this method to gene expression data, meaningful clusters are discovered which assists to capture aspects of gene association patterns. Thus, significant genes containing useful information for gene classification and identification are selected. In the following, the proposed algorithm is employed to six gene expression data sets and a comparison is made with several well-known gene selection methods. Experiments show that the greedy correlation measure based attribute clustering algorithm, noted as GCMACA, is more capable of discovering meaningful clusters of genes. Through selecting a subset of genes which have a high significant multiple correlation value with others within clusters, informative genes can be acquired and gene expression of different categories can be identified as well.