Weight Computing in Competitive K-Means Algorithm

Abstract

This paper presents Weight Computing in Competitive K-Means Algorithm which is derived from Improved K-means method and subspace clustering. By adding weights to the objective function, the contributions from each feature of each clustering could simultaneously minimize the separations within clusters and maximize the separation between clusters. The experiments described in this paper confirm good performance of the proposed algorithm.