A novel feature measure for fuzzy clustering algorithm on microarray data

Fuzzy clustering algorithm is employed in gene microarray analysis to discover the strength of the association between genes and different clusters. Gene-based fuzzy clustering algorithm just employs all instances´ values of a certain gene as this gene´s features. In some sense, the original feature vector can hardly provide comprehensive discriminative information of the gene. In this paper, a novel feature vector by the proposed measure for each gene is employed in fuzzy clustering algorithm. The proposed feature vector can provide information about the influence of a given gene for the overall shape of clusters. By analysis and experiment upon microarray data sets, the performance of the fuzzy clustering algorithm based on proposed feature vector is compared with that of some classical clustering algorithms. The results demonstrate that the fuzzy clustering algorithm based on proposed feature vector is capable of obtaining better clusters than other contrast algorithms. The results by classifiers based on different clustering algorithms demonstrate that the proposed feature vector can get the same or better accuracy than the original feature vector.