A merging Fuzzy ART clustering algorithm for overlapping data

Abstract

Real-world datasets usually involve class overlap. It has been observed that, in general, the performance of clustering algorithms degrade with the increasing overlapping degree. The main challenge for clustering overlapping data is the determination of the appropriate number of clusters and division of the overlapping region. This paper proposes a novel method based on Fuzzy ART clustering to handle the overlapping data without demanding a priori the number of clusters. With the use of over-clustering and merging mechanism, Merging Fuzzy ART (MFuART) generates the number of clusters automatically and with good cluster quality.