Multi-view clustering algorithm based on fuzzy C-means

被引：1

作者：

Yang, Xinxin ^{[1
]}

Huang, Shaobin ^{[1
]}

机构：

[1] College of Computer Science and Technology, Harbin Engineering University, Harbin

来源：

Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2015年 / 46卷 / 06期

关键词：

Data mining; Fuzzy C-means; Multi-view clustering;

D O I：

10.11817/j.issn.1672-7207.2015.06.021

中图分类号：

学科分类号：

摘要：

Considering that most exiting multi-view clustering algorithms focusing on hard-partition clustering methods, which are not suitable for analyzing dataset with overlapping clusters, a multi-view clustering algorithm based on fuzzy C-means (FCM-MVC) was developed. The membership degree was used to describe the relation between objects and clusters, so FCM-MVC algorithm could more truely describe clustering results of dataset with overlapping clusters. FCM-MVC algorithm simultaneously incorporated fearture information in multi-view space and automatically computes weight of each view. The results show that FCM-MVC can analyze overlapping clusters effectively and the precision of clustering results of FCM-MVC are superior to the three representative algorithms. ©, 2015, Central South University of Technology. All right reserved.

引用

页码：2128 / 2133

页数：5

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