局部标准差优化的密度峰值聚类算法OA北大核心CSTPCD

DPC(clustering by fast search and find of density peaks)algorithm is a density based clustering algorithm.It is one of the milestone clustering algorithms.It can find any arbitrary shapes of clusters embedded within any dimensional spaces.However,its local density definition of a point is not appropriate for simultaneously detecting the cluster centers of dense and sparse clusters,nor detecting the sparse and dense clusters subsequently.In addition,its one-step assignment strategy leads to a fatal problem,that is,once a point is assigned to an incorrect cluster,there are more subsequent points being assigned erroneously,resulting in the domino effect.To address the aforementioned problems,this paper redefines the local density of a point based on the local standard deviation,and proposes a two-step assignment strategy,resulting in the ESDTS-DPC algorithm.The ESDTS-DPC algorithm is compared with the original DPC and its variations including KNN-DPC,FKNN-DPC,DPC-CE and the classic density based clustering algorithm,such as DBSCAN.The extensive experiment results demonstrate superiority of the proposed ESDTS-DPC in detecting the clustering within a dataset.