Self-splitting competitive learning: A new on-line clustering paradigm

Abstract—Clustering in the neural-network literature is gener-ally based on the competitive learning paradigm. This paper ad-dresses two major issues associated with conventional competitive learning, namely, sensitivity to initialization and difficulty in deter-mining the number of prototypes. In general, selecting the appro-priate number of prototypes is a difficult task, as we do not usually know the number of clusters in the input data a priori. It is there-fore desirable to develop an algorithm that has no dependency on the initial prototype locations and is able to adaptively generate prototypes to fit the input data patterns. In this paper, we present a new, more powerful competitive learning algorithm, self-split-ting competitive learning (SSCL), that is able to find the natural number of clusters based on the one-prototype-take-one-cluster (OPTOC) paradigm and a self-splitting validity measure. It starts with a single prototype randomly initialized in the feature space and splits adaptively during the learning process until all clusters are found; each cluster is associated with a prototype at its center. We have conducted extensive experiments to demonstrate the ef-fectiveness of the SSCL algorithm. The results show that SSCL has the desired ability for a variety of applications, including unsuper-vised classification, curve detection, and image segmentation. Index Terms—Clustering, competitive learning, one-proto-type-take-one-cluster (OPTOC), self-splitting, unsupervised learning, validity measure, winner-take-all (WTA). I