关键词: 密度峰值聚类, 密度二分法, 决策图, 高密度点

Abstract: Density Peaks Clustering（DPC） is a famous cluster algorithm for various data, regardless of their shapes or features. It has been widely studied and applied to solve problems in many fields in recent years. However, its clustering effect is reduced when the densities of the cluster centers differ greatly, or there are many peaks of density in a certain cluster. To address it, a density peaks clustering method based on density dichotomies is proposed. Firstly, the global average density of each point is obtained and the data are divided into two groups according to high density and low density. Secondly, it identifies the clustering centers according to the decision diagram of high density points and then merges the clustering centers if it is within reachable distance. Finally, the high density points and the low density points are assigned to the appropriate clustering centers according to the strategy proposed in this paper. Experiments on several synthetic and real datasets show that the clustering results of the proposed algorithm are better than those of existing DPC algorithms.

Key words: Density Peaks Clustering（DPC）, density dichotomy, decision diagram, high density points