doi:10.16186/j.cnki.1673-9787.2021110037

Received:2021/11/11

Revised:2022/05/30

Published:2024/05/15

Multi-view fuzzy clustering algorithm based on ECM

LIU Yongli, CHANG Ran

School of Computer Science and Technology， Henan Polytechnic University， Jiaozuo 454000， Henan， China

Abstract:    Objectives Traditional clustering algorithms mostly belong to the category of single-view clustering.Today，as the data structure is becoming increasingly complex，single-view clustering is becoming more and more challenging to provide a comprehensive and accurate representation of knowledge for datasets. Notably，while the evidential C-means clustering algorithm demonstrates a relatively outstanding ability to reveal data structure，it is limited by its design for single views，which makes it relatively weak in providing an comprehensive description of datasets.  Methods To address this issue，a multi-view fuzzy clustering algorithm based on the evidential C-means clustering was proposed.This algorithm created credential partitions under the theoretical framework of the belief function，then calculated the weights of various features in different views，assigned these weights to respective partitions across different perspectives，thereby generated the final clustering results. On the one hand，it extended the concepts of hard partition，fuzzy partition，and possibilistic partition，inherited the advantages of both the evidential C-Means clustering algorithm and multi-view fuzzy clustering， mined valuable information from different views. On the other hand，it could automatically allocate weights according to the importance of each view， thereby improved the clustering accuracy.  Results To verify the clustering performance of the proposed algorithm， comparative experiments were conducted on four multi-view datasets against five other algorithms. The experiments included three parts， clustering accuracy，clustering efficiency， and parameter analysis.Experimental results showed that the proposed algorithm performed well in terms of three quantitative metrics：clustering accuracy，F-measure， and normalized mutual information， indicating superiority over the comparative algorithms in terms of clustering accuracy. In terms of clustering efficiency，except for slightly longer clustering times on certain datasets due to excessive iterations，the overall performance was close to the best among the comparative algorithms.      Conclusions These outcomes further substantiated the effectiveness of the proposed algorithm when dealing with multi-view datasets.  

Key words:clustering;multi-view;feature;weight;accuracy