Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 10
2943-2947

Clustering ensemble algorithm based on multi-granulation rough set

Yu Peiqiu1,2
Li Jinjin1
Lin Guoping1,2
1. School of Mathematics & Statistics, Minnan Normal University, Zhangzhou Fujian 363000, China
2. Laboratory of Granular Computing, Zhangzhou Fujian 363000, China
DOI: 10.19734/j.issn.1001-3695.2018.04.0217

Abstract

Existing clustering ensemble algorithm starts from the perspective of cluster members, if all the cluster members are used, the ensemble result is affected by the inferior members. If the cluster members are selected and then used in ensemble, the selected strategy has subjectivity. To avoid these two limitations to some extent, from the perspective of elements, this paper proposed a new clustering fusion method. It selected a part of class-determined elements through multi-granulation rough sets with incongruous decisions, and then used this part of the elements to generate a new clustering. Multi-granulation rough set model with incongruous decisions could describe the phenomenon of inconsistent decisions with consistent attribute set. Therefore, this paper proposed a model of multi-granulation rough set with incongruous decisions and a clustering ensemble algorithm based on the model. First of all, it ran a K-means clustering algorithm several times on the data set in the case and generated multiple granule structures. Next, it calculated inclusion degrees among all the granulations, and obtained the matrix of inclusion degree. Used Otsu's method to generate a threshold, then got several group of granulation that met the threshold condition. According to the model of multi-granulation rough set with incongruous decision, it obtained lower and upper approximations. Finally, classified the elements of lower approximation and boundary separately to obtained a clustering that has been fused. The experiments show that the algorithm has a high time efficiency and robustness, which improves the result of K-means clustering.

Foundation Support

福建省自然科学基金资助项目(2016J01315,2017J01507)
国家自然科学基金资助项目(61379021,11871259)
国家青年科学基金资助项目(61603173)
浙江省海洋大数据挖掘与应用重点实验室开放课题(OBDMA201603)
2017年福建省中青年教师教育科研项目(JAT170340)
福建省数学类研究生教育创新基地资助项目(1013-313009)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.04.0217
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 10
Section: Algorithm Research & Explore
Pages: 2943-2947
Serial Number: 1001-3695(2019)10-013-2943-05

Publish History

[2019-10-05] Printed Article

Cite This Article

于佩秋, 李进金, 林国平. 基于多粒度粗糙集的聚类融合方法 [J]. 计算机应用研究, 2019, 36 (10): 2943-2947. (Yu Peiqiu, Li Jinjin, Lin Guoping. Clustering ensemble algorithm based on multi-granulation rough set [J]. Application Research of Computers, 2019, 36 (10): 2943-2947. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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