Overlapping Clustering: A Review

Data Clustering or unsupervised classification is one of the main research area in Data Mining. Partitioning Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard (crisp) partitioning techniques where each object is assigned to one cluster. Other algorithms utilise overlapping techniques where an object may belong to one or more clusters. Partitioning algorithms that overlap include the commonly used Fuzzy K-means and its variations. Other more recent algorithms reviewed in this paper are the Overlapping K-Means (OKM), Weighted OKM (WOKM) the Overlapping Partitioning Cluster (OPC) and the Multi-Cluster Overlapping K-means Extension (MCOKE). This review focuses on the above mentioned partitioning methods and future direction in overlapping clustering is highlighted in this paper.