A Novel Density Peaks Clustering Algorithm Based on Local Reachability Density

被引：2

作者：

Wang, Hanqing ^{[1
]}

Zhou, Bin ^{[1
]}

Zhang, Jianyong ^{[2
]}

Cheng, Ruixue ^{[2
]}

机构：

[1] Southeast Univ, Sch Energy & Environm, Sipailou Rd 2, Nanjing, Jiangsu, Peoples R China

[2] Teesside Univ, Sch Comp Engn & Digital Technol, Middlesbrough TS1 3BA, Cleveland, England

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 2020年 / 13卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Clustering algorithm; Density peaks clustering; Local reachability density; Domino effect;

D O I：

10.2991/ijcis.d.200603.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A novel clustering algorithm named local reachability density peaks clustering (DPC) which uses local reachability density, improve the performance of the density peaks clustering algorithm (DPC) is proposed in this paper. This algorithm enhances robustness by removing the cutoff distance dc which is a sensitive parameter from the DPC. In addition, anew allocation strategy is developed to eliminate the domino effect, which often occurs in DPC. The experimental results confirm that this algorithm is feasible and effective. (C) 2020 The Authors. Published by Atlantis Press SARL.

引用

页码：690 / 697

页数：8

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