A novel density peaks clustering algorithm based on Hopkins statistic

作者：

Highlights：

• A novel density peaks clustering based on Hopkins Statistic (DPC-AHS) is proposed.

• DPC-AHS can automatically find clusters and centers without manual participation.

• A cluster validity index AHS with low complexity is designed to evaluate clustering.

• Experiments and discussions on various datasets show the effectiveness of our method.

• DPC-AHS requires only one parameter and can be applied to high dimensional data.

摘要

•A novel density peaks clustering based on Hopkins Statistic (DPC-AHS) is proposed.•DPC-AHS can automatically find clusters and centers without manual participation.•A cluster validity index AHS with low complexity is designed to evaluate clustering.•Experiments and discussions on various datasets show the effectiveness of our method.•DPC-AHS requires only one parameter and can be applied to high dimensional data.

论文关键词：Clustering,Cluster validity index (CVI),Cluster center,Hopkins statistic,Density peaks

论文评审过程：Received 26 September 2021, Revised 10 March 2022, Accepted 11 March 2022, Available online 22 March 2022, Version of Record 20 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116892