关键词: 聚类分析, CDbw评价指标, 密度峰值, 密度聚类, 人工蜂群算法

Abstract: Aiming at the problem that value of d_c was difficult to select and the poor effect of neighborhood principle aggregation operation in low density area, we proposed a density peaks clustering (DPC) algorithm based on artificial bee colony and CDbw clustering index optimization, which realized automatic identification and reasonable clustering of data points between 
clusters, and solved the defect of DPC in class identification of data points between clusters. Experiment results show that the BeeDPC algorithm has advantages of automatic identification and reasonable clustering of data points between clusters, automatic identification of cluster centers and the number of clusters and dealing with arbitrary distributed data sets.

Key words: cluster analysis, CDbw evaluation index, density peak, density clustering, artificial bee colony (ABC) algorithm