A dynamic niching clustering algorithm based on individual-connectedness and its application to color image segmentation

Abstract

In this paper, a dynamic niching clustering algorithm based on individual-connectedness (DNIC) is proposed for unsupervised classification with no prior knowledge. It aims to automatically evolve the optimal number of clusters as well as the cluster centers of the data set based on the proposed adaptive compact k-distance neighborhood algorithm. More specifically, with the adaptive selection of the number of the nearest neighbor and the individual-connectedness algorithm, DNIC often achieves several sets of connecting individuals and each set composes an independent niche. In practice, each set of connecting individuals corresponds to a homogeneous cluster and this ensures the separability of an arbitrary data set theoretically. An application of the DNIC clustering algorithm in color image segmentation is also provided. Experimental results demonstrate that the DNIC clustering algorithm has high performance and flexibility.