Fuzzy Cluster Quality Index using Decision Theory

Clustering can be defined as the process of grouping physical or abstract objects into classes of similar objects. It’s an unsupervised learning problem of organizing unlabeled objects into natural groups in such a way objects in the same group is more similar than objects in the different groups. Conventional clustering algorithms cannot handle uncertainty that exists in the real life experience. Fuzzy clustering handles incompleteness, vagueness in the data set efficiently. The goodness of clustering is measured in terms of cluster validity indices where the results of clustering are validated repeatedly for different cluster partitions to give the maximum efficiency i.e. to determine the optimal number of clusters. Especially, fuzzy clustering has been widely applied in a variety of areas and fuzzy cluster validation plays a very important role in fuzzy clustering. Since then Fuzzy clustering has been evaluated using various cluster validity indices. But primary indices have used geometric measures; this paper proposes decision theoretic measure for fuzzy clustering