An emerging hybrid approach based on intuitionistic fuzzy c-means with intuitionistic particle swarm optimization for microarray data

Due to enormous growth in gene (gene function and regulatory mechanisms) with the exposure in advanced techniques, handling high dimensional data still becomes a continuous research. Data mining plays a vital role for inferring hidden information from voluminous data set to retrieve knowledgeable information. Although fuzzy approaches are already implemented in bio-inspirational concept, it lacks to process efficiently in case of incomplete or inconsistent data set. This leads to increased false alarm rate. 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 IFCM and IFPSO to increase the detection accuracy and decrease the false alarm ratio considerably. To find similarity among objects and cluster centers intuitionistic based similarity measure is used. Intuitionistic fuzzy particle swarm optimization optimizes the working of the Intuitionistic FCM. Experimental results of proposed approach shows better results when compared with the existing methods.