Simulated annealing based automatic fuzzy clustering combined with ANN classification for analyzing microarray data

Microarray technology has made it possible to monitor the expression levels of many genes simultaneously across a number of experimental conditions. Fuzzy clustering is an important tool for analyzing microarray gene expression data. In this article, a real-coded Simulated Annealing (VSA) based fuzzy clustering method with variable length configuration is developed and combined with popular Artificial Neural Network (ANN) based classifier. The idea is to refine the clustering produced by VSA using ANN classifier to obtain improved clustering performance. The proposed technique is used to cluster three publicly available real life microarray data sets. The superior performance of the proposed technique has been demonstrated by comparing with some widely used existing clustering algorithms. Also statistical significance test has been conducted to establish the statistical significance of the superior performance of the proposed clustering algorithm. Finally biological relevance of the clustering solutions are established.