A study of various Fuzzy Clustering Algorithms

Abstract : In data mining clustering techniques are used to group together the objects showing similar characteristics within the same cluster and the objects demonstrating different characteristics are grouped into clusters. Clustering approaches can be classified into two categories namely- Hard clustering and Soft clustering. In hard clustering data is divided into clusters in such a way that each data item belongs to a single cluster only while soft clustering also known as fuzzy clustering forms clusters such that data elements can belong to more than one cluster based on their membership levels which indicate the degree to which the data elements belong to the different clusters. Fuzzy C-Means is one of the most popular fuzzy clustering techniques and is more efficient that conventional clustering algorithms. In this paper we present a study on various fuzzy clustering algorithms such as Fuzzy C-Means Algorithm (FCM), Possibilistic C-Means Algorithm (PCM), Fuzzy Possibilistic C-Means Algorithm (FPCM) and Possibilistic Fuzzy C Means Algorithm (PFCM) with their respective advantages and drawbacks.