Density peak clustering algorithm based on interval shadowed sets

被引：0
|
作者：
Chen Y. [1 ]
Zhang Q. [1 ]
Yang J. [1 ]
机构：
[1] Chongqing Key Laboratory of Computational Intelligence, Chong-qing University of Posts and Telecommunications, Chongqing
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Density Peak; Fuzzy Set; Local Density; Shadowed Set; Three-Way Decision;
D O I：
10.16451/j.cnki.issn1003-6059.201906006
中图分类号：
学科分类号：
摘要：
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. © 2019, Science Press. All right reserved.
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页码：531 / 544
页数：13
相关论文
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