Clustering fuzzy objects using ant colony optimization

This paper deals with the problem of grouping a set of objects into clusters. The objective is to minimize the sum of squared distances between objects and centroids. This problem is important because of its applications in different areas. In prior literature on this problem, attributes of objects have often been assumed to be crisp numbers. However, since in many realistic situations object attributes may be vague and should better be represented by fuzzy numbers, we are interested in the generalization of the minimum sum-of-squares clustering problem with the attributes being fuzzy numbers. Specifically, we consider the case where an object attribute is a triangular fuzzy number. The problem is first formulated as a fuzzy nonlinear binary integer programming problem based on a newly proposed dissimilarity measure, and then solved by developing and demonstrating a problem-specific ant colony optimization algorithm. The proposed algorithm is evaluated by computational experiments.