Original scientific paper

https://doi.org/10.17559/TV-20191205093209

A Wolf Pack Optimization Theory Based Improved Density Peaks Clustering Approach

Jinlong Tian ; School of Computer Science and Engineering, Changchun University of Science and Technology, Changchun130022, China
Jianping Zhao* ; School of Computer Science and Engineering, Changchun University of Science and Technology, Changchun130022, China
Wei Zhou ; School of Computer Science and Engineering, Changchun University of Science and Technology, Changchun130022, China
Xuming Han ; College of Information Science Technology, Jinan University, Guangzhou, 510632, China
Limin Wang* ; School of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou, 510521, China

Abstract

In view of the problem that the Density Peaks Clustering (DPC) algorithm needs to manually set the parameter cut-off distance (dc) we propose a Wolf Pack optimization theory based Density Peaks Clustering approach (WPA-DPC). Firstly, we introduce dc parameter into the Wolf Pack Algorithm (WPA) to speed up the search. Secondly, we introduce the WPA into the DPC algorithm; the cut-off distance is used as the location of the wolf group. Finally, we make silhouette index in the search process as the fitness value, and the optimal location of the wolf group is the parameter value at the end. The simulation results show that compared with the traditional Density Peaks Clustering algorithm, the proposed algorithm is closer to the true clustering number. According to the evaluation results of silhouette and f-measure, the quality of clustering and the accuracy are greatly improved.

Keywords

cut-off distance; density peaks clustering; evaluation index; wolf pack algorithm

Hrčak ID:

248227

URI

https://hrcak.srce.hr/248227

Publication date:

19.12.2020.

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