Real Time Clustering Model

Abstract: This paper focuses on the development of a dynamic system model in unsupervised learning environment. This adaptive dynamic system consists of a set of energy functions which create valleys for representing clusters. Each valley represents a cluster of similar input patterns. The system includes a dynamic parameter for the clustering vigilance so that the cluster size or the quantizing resolution can be adaptive to the density of the input patterns. It also includes a factor for invoking competitive exclusion among the valleys; forcing only one label to be assigned to each cluster. Through several examples of different pattern clusters, it is shown that the model can successfully cluster these types of input patterns and form different sizes of clusters according to the size of the input patterns.