A novel similarity measure for data clustering

Abstract

A novel similarity measure, proposed for clustering data with arbitrary distribution shapes, is developed. Such a new measure of similarity is employed in a dynamic model to collectively measure similarity among pattern vectors, which can help to achieve a more robust clustering performance than using the existing measures that are staticly and individually based on the distances among the isolated pairwise data. The experiment results demonstrated that the proposed neural network based on the new similarity measure has the capability to robustly and quickly cluster data on which Cluster-Detection-and-Labeling neural network fails.