基于区间阴影集的密度峰值聚类算法

doi:10.16451/j.cnki.issn1003-6059.201906006

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Abstract

To narrow the discrepancy between a fuzzy set and its induced shadowed set, a shadowed set model, interval shadowed set, is proposed based on fuzzy entropy. Grounded on the interval shadowed set model, an improved density peak clustering algorithm is proposed to optimize the noise detection strategy of the classical algorithm. To detect the noise, the two-way clustering result of classical algorithm is transformed into three-way clustering result by introducing interval shadowed set model. Finally, comparison experiments on classical artificial datasets and UCI datasets show that the improved algorithm distributes the objects of any dimension and scale more reasonably to the corresponding clusters, and it has good robustness to noise data.

Key words： Fuzzy Set Shadowed Set Three-Way Decision Local Density Density Peak

Received: 22 March 2019

ZTFLH:

TP 301.6

About author:: (CHEN Yuhong(Corresponding author), master student. His research interests include fuzzy sets, shadowed sets, three-way decisions and clustering analysis.)(ZHANG Qinghua, Ph.D., professor. His research interests include rough sets, fuzzy sets, granular computing and uncertain information processing.)(YANG Jie, Ph.D. candidate. His research interests include data mining, machine lear-ning, three-way decisions and rough sets.)

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	CHEN Yuhong
	ZHANG Qinghua
	YANG Jie

Cite this article:

CHEN Yuhong,ZHANG Qinghua,YANG Jie. Density Peak Clustering Algorithm Based on Interval Shadowed Sets[J]. , 2019, 32(6): 531-544.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201906006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I6/531

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