A robust density peaks clustering algorithm using fuzzy neighborhood

Abstract

The density peaks (DP) clustering approach, a novel density-based clustering algorithm, detects clusters with arbitrary shape. However, this method uses a crisp neighborhood relation to calculate local density. It cannot identify the different values of the neighborhood membership degrees of the points with respect to different distances from core point. The proposed FN-DP (fuzzy neighborhood density peaks) clustering algorithm uses fuzzy neighborhood relation to define the local density in FJP (fuzzy joint points) algorithm. The proposed algorithm integrates the speed of DP clustering algorithm with the robustness of FJP algorithm. The experimental results illustrate the superior performance of our algorithm compared with the DP clustering approach.