WCSE

A Density Peak Clustering Method based on Improved Siphon Effect

Abstract— To solve the problem that the density peak clustering algorithm needs to manually select the clustering center, a density peak clustering algorithm based on improved siphon effect (IDPC) is designed. IDPC completes the clustering of research objects with various distribution shapes by removing the maximum cluster center weight in the descending sequence, calculating and iterating the potential difference, and selecting the relative equilibrium point. A comprehensive comparative experiment is carried out on UCI data set. The experimental results show that IDPC can accurately and automatically select the cluster center and cluster number, improve the performance indexes such as Fowles mallows score, Rand index and adjusted mutual information, and can adaptively process data sets with low dimension and various distribution shapes.

Index Terms—peak clustering, siphon effect, dichotomy, density clustering, unsupervised learning

Jiyuan Liu
School of Computer Science, Qufu Normal University, Rizhao Shandong 276826, CHINA
Xiaona Xia
Faculty of Education, Qufu Normal University, Qufu, Shandong, 273165; Chinese Academy of Education Big Data, Qufu Normal University, Qufu, Shandong, 273165; School of Computer Science, Qufu Normal University, Rizhao, Shandong, 276826, CHINA

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Cite:Jiyuan Liu, Xiaona Xia, "A Density Peak Clustering Method based on Improved Siphon Effect, " Proceedings of 2022 the 12th International Workshop on Computer Science and Engineering (WCSE 2022), pp. 268-274, June 24-27, 2022.