A clustering algorithm based on feature weighting fuzzy compactness and separation

Abstract

Aiming at improving the well-known fuzzy compactness and separation algorithm (FCS), this paper proposes a new clustering algorithm based on feature weighting fuzzy compactness and separation (WFCS). In view of the contribution of features to clustering, the proposed algorithm introduces the feature weighting into the objective function. We first formulate the membership and feature weighting, and analyze the membership of data points falling on the crisp boundary, then give the adjustment strategy. The proposed WFCS is validated both on simulated dataset and real dataset. The experimental results demonstrate that the proposed WFCS has the characteristics of hard clustering and fuzzy clustering, and outperforms many existing clustering algorithms with respect to three metrics: Rand Index, Xie-Beni Index and Within-Between(WB) Index.