MICROARRAY GENE EXPRESSION MINING USING PARTICLE SWARM OPTIMIZATION AND MODIFIED K- MEANS AND K-NEAREST ALGORITHM

Abstract

These days, microarray gene expression data are playing an essential role in cancer classifications. However, due to the availability of small number of effective samples compared to the large number of genes in microarray data, many computational methods have failed to identify a small subset of important genes. Therefore, it is a challenging task to identify small number of disease-specific significant genes related for precise diagnosis of cancer sub classes. In this paper, particle swarm optimization (PSO) method along with adaptive K-nearest neighborhood (KNN) based gene selection technique are proposed to distinguish a small subset of useful genes that are sufficient for the desired classification purpose. A proper value of K would help to form the appropriate numbers of neighborhood to be explored and hence to classify the dataset accurately. Thus, a heuristic for selecting the optimal values of K efficiently, guided by the classification accuracy is also proposed. The fuzzy c-means clustering algorithm (FCM) is applied extensively. However, it can easily be trapped in a local optimum, and also strongly depends on initialization. Therefore, a method of fuzzy clustering by using genetic algorithm is proposed in this paper. Genetic algorithm refers to choose the number of cluster centers and the data that are cluster centers firstly, and clustering analysis is processed by FCM consequently. Experiment results show that the method can search global optimum partly to make the clustering analysis more rational.