基于簇中心预选策略的三支决策密度峰值聚类算法OACSTPCD

Aiming at the uncertainty problem that the CFSFDP(clustering by fast search and find of density peaks)algorithm cannot automatically select the clustering center,in this paper,we propose a three-way decision-based density peak clustering algorithm with clustering centers preselection(TDPC)by incorporating the three-way decision theory.Firstly,the statistical characteristics of density and distance are used to divide the data objects into core region,boundary region and trivial region.The qualified cluster cen-ters are assigned to the core region,and the suspected cluster centers that are difficult to determine are placed in the boundary region.Then the defined k-reachable region and discriminant criterion are used to analyze the suspected cluster centers,and the actual clus-ter centers are subsequently selected.The proposed algorithm can effectively solve the problem of automatic determination of cluster centers in density peak clustering algorithm.The proposed algorithm is evaluated on two synthetic datasets and four UCI(University of California,lrvine)public datasets.Comparing to the CFSFDP algorithm and the DBSCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm,TDPC demonstrated clustering performance that is on par with or superior to the optimal algo-rithm across various clustering evaluation indexes,including silhouette coefficient,DB(Davies-Bouldin)index,adjusted mutual in-formation,adjusted rand index,FM(Fowlkes-Mallows)index,homogeneity,and completeness.These results indicate that TDPC outperforms the comparison algorithms in terms of comprehensive clustering performance,and highlight its good clustering feasibili-ty and effectiveness.