Decision Theoretic Evaluation of Rough Fuzzy Clustering

Clustering is the process of organizing dissimilar objects into natural groups in such a way objects in the same group is more similar than objects in the different groups. Since we know clustering is an unsupervised learning problem, typical clustering algorithms not achieving its end to handle uncertainty that exists in the real life experience. Though fuzzy clustering handles incompleteness and vagueness in the data set efficiently, it is highly descriptive than hard clustering algorithm. Rough clustering algorithm is the popular soft clustering technique which uses rough set to handle uncertainty. In Rough Fuzzy clustering, each cluster is represented by centroid, crisp lower approximation and fuzzy boundary. Clustering undergoes sequence of partitions where cluster evaluation is the final step in clustering process. Efficient clustering structure can be obtained through validity measures. Various validity measures have been proposed to evaluate rough fuzzy clustering. Since those measures are Geometric measures, this paper proposes decision theoretic measure for validating rough fuzzy clustering structure.