关键词: 密度峰值, 聚类, 簇中心点, 基尼指数

Abstract: Density Peaks Clustering（DPC） is a density-based clustering algorithm, which has the advantage of not needing to specify clustering parameters and discovering non-spherical clusters. In this paper, an adaptive truncation method based on Gini index is proposed to solve the problem that the density peak algorithm can not effectively deal with each scene by calculating the cutoff distance [dc], and the density peak algorithm manually selects the clustering center to get the actual clustering center. Distance [dc] and automatic clustering center method can effectively solve the defects of traditional DPC algorithm which can not handle the complex data set. The algorithm firstly cuts off the distance through Gini index, then calculates the cluster center weights of each point, and then uses the change of slope to find the critical point. This strategy effectively avoids the errors caused by manual selection of clustering centers by decision graph. Experiments show that the new algorithm not only can automatically determine the clustering center, but also has higher accuracy than the original algorithm.

Key words: density peak, clustering, cluster center point, Gini index