Generalizing Distance Functions for Fuzzy c-Means Clustering

Abstract

Fuzzy $$c$$ -means (FCM) is a well-known partitional clustering method, which allows an object to belong to two or more clusters with a membership grade between zero and one. Recently, due to the rich information conveyed by the membership grade matrix, FCM has been widely used in many real-world application domains where well-separated clusters are typically not available. In addition, people also recognize that the simple centroid-based iterative procedure of FCM is very appealing when dealing with large volume data.