Collaborative Fuzzy Clustering From Multiple Weighted Views

Yizhang Jiang, Fu Lai Korris Chung, Shitong Wang, Zhaohong Deng, Jun Wang, Pengjiang Qian

Research output: Journal article publicationJournal articleAcademic researchpeer-review

243 Citations (Scopus)


Clustering with multiview data is becoming a hot topic in data mining, pattern recognition, and machine learning. In order to realize an effective multiview clustering, two issues must be addressed, namely, how to combine the clustering result from each view and how to identify the importance of each view. In this paper, based on a newly proposed objective function which explicitly incorporates two penalty terms, a basic multiview fuzzy clustering algorithm, called collaborative fuzzy c-means (Co-FCM), is firstly proposed. It is then extended into its weighted view version, called weighted view collaborative fuzzy c-means (WV-Co-FCM), by identifying the importance of each view. The WV-Co-FCM algorithm indeed tackles the above two issues simultaneously. Its relationship with the latest multiview fuzzy clustering algorithm Collaborative Fuzzy K-Means (Co-FKM) is also revealed. Extensive experimental results on various multiview datasets indicate that the proposed WV-Co-FCM algorithm outperforms or is at least comparable to the existing state-of-the-art multitask and multiview clustering algorithms and the importance of different views of the datasets can be effectively identified.
Original languageEnglish
Article number6862861
Pages (from-to)688-701
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number4
Publication statusPublished - 1 Apr 2015


  • Collaborative clustering
  • fuzzy c-means
  • multiple view clustering
  • objective function

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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