Improved Fuzzy Clustering Method Based on Intuitionistic Fuzzy Particle Swarm Optimization

Recent advances in technology has led to huge growth in generating high dimensional data sets by capturing millions of facts in various fields, time phases, localities and brands. Microarray data contains gene expression from thousands of genes (features) from only tens of hundreds of samples. The rich source of information generated from microarray experiments often consist of incomplete and/or inconsistent data. Data mining is a powerful technology that automates the process of discovering hidden patterns. Traditional fuzzy clustering approaches are available which lacks to process efficiently in case of incomplete or inconsistent data. It has high influence over the resulting partitions. In this proposed approach, the degree of membership to indeterminacy is extended by adopting the concept of generalization of fuzzy logic, which is known as intuitionistic fuzzy logic. This paper proposes a hybrid approach for clustering high dimensional data set using FCM and Intuitionistic Fuzzy Particle Swarm Optimization (IFPSO) to overcome the local convergence problem. To find similarity among objects and cluster centers intuitionistic based similarity measure is used. Intuitionistic fuzzy particle swarm optimization optimizes the working of the Fuzzy cmeans algorithm. Experimental results of proposed approach shows better results when compared with the existing methods.