Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32321
Title: Collaborative fuzzy clustering from multiple weighted views
Authors: Jiang, Y
Chung, FL 
Wang, S
Deng, Z
Wang, J
Qian, P
Keywords: Collaborative clustering
Fuzzy c-means
Multiple view clustering
Objective function
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2015, v. 45, no. 4, 6862861, p. 688-701 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: 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.
URI: http://hdl.handle.net/10397/32321
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2014.2334595
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