On using tabu search for fuzzy clustering analysis

Clustering is an important technique for discovering the inherent structure in a given data set without any 'priori' knowledge. Fuzzy clustering analysis is to assign objects to a given number of clusters with respect to some criteria such that each object may belong to more than one cluster with different degrees of membership. In this article, a new fuzzy clustering method based on tabu search called Improved Tabu Search Fuzzy Clustering (ITSFC) is proposed to find the proper clustering of data sets. In the ITSFC approach, a fuzzy c-means operation is developed to fine-tune the clustering solution obtained in the process of iterations and a divide-and-merge operation is designed to establish the neighborhood. Experimental results on two artificial and four real life data sets are given to illustrate the superiority of the proposed algorithm over a tabu search clustering algorithm and an artificial bee colony clustering algorithm. © 2011 Academic Journals.