Self-organizing mountain method for clustering

Abstract

A self-organizing mountain method (SOMM) is presented. SOMM incorporates the Possibilistic C-Means (PCM) technique and the concept of the mountain method to perform clustering. By self-organizing we mean that parameters are data-driven, the terrain of each cluster (or mountain) is estimated, the precise center of each cluster and the terminating condition are determined by the input nature. In addition, SOMM is robust even when a large number of outliers/noises is presented. The simulation results show that the robust clustering can be obtained for various Gaussian clusters and uniform clusters, respectively.