Collaborative feature-weighted multi-view fuzzy c-means clustering
作者：
Highlights：
• Fuzzy c-means (FCM) clustering had been extended for handling multi-view data.
• We propose a novel multi-view FCM (MVFCM) clustering algorithm with view and feature weights based on collaborative learning, called Co-FW-MVFCM.
• The proposed Co-FW-MVFCM contains a two-step schema that includes a local step and a collaborative step.
• The Co-FW-MVFCM can give feature reduction to exclude redundant feature components during clustering processes.
• Comparisons among Co-FW-MVFCM and existing MVFCM algorithms actually demonstrate the effectiveness and usefulness of Co-FW-MVFCM.
摘要
•Fuzzy c-means (FCM) clustering had been extended for handling multi-view data.•We propose a novel multi-view FCM (MVFCM) clustering algorithm with view and feature weights based on collaborative learning, called Co-FW-MVFCM.•The proposed Co-FW-MVFCM contains a two-step schema that includes a local step and a collaborative step.•The Co-FW-MVFCM can give feature reduction to exclude redundant feature components during clustering processes.•Comparisons among Co-FW-MVFCM and existing MVFCM algorithms actually demonstrate the effectiveness and usefulness of Co-FW-MVFCM.
论文关键词：Clustering,Fuzzy c-means (FCM),Multi-view FCM (MVFCM),Collaborative learning,Feature weights,Collaborative feature-weighted MVFCM (Co-FW-MVFCM),Feature reduction
论文评审过程：Received 8 August 2020, Revised 12 January 2021, Accepted 19 May 2021, Available online 28 May 2021, Version of Record 6 June 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108064
