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Open Access

TW-Co-MFC: Two-Level Weighted Collaborative Fuzzy Clustering Based on Maximum Entropy for Multi-View Data

Jie Hu( ), Yi Pan, Tianrui Li, Yan Yang
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.
Department of Computer Science, Georgia State University, Atlanta, GA 30302-3994, USA.
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Abstract

In recent years, multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis of multi-view data in practical applications. Despite the significant advances in multi-view clustering, two challenges still need to be addressed, i.e., how to make full use of the consistent and complementary information in multiple views and how to discriminate the contributions of different views and features in the same view to efficiently reveal the latent cluster structure of multi-view data for clustering. In this study, we propose a novel Two-level Weighted Collaborative Multi-view Fuzzy Clustering (TW-Co-MFC) approach to address the aforementioned issues. In TW-Co-MFC, a two-level weighting strategy is devised to measure the importance of views and features, and a collaborative working mechanism is introduced to balance the within-view clustering quality and the cross-view clustering consistency. Then an iterative optimization objective function based on the maximum entropy principle is designed for multi-view clustering. Experiments on real-world datasets show the effectiveness of the proposed approach.

Keywords

multi-view clusteringfuzzy clusteringcollaborativeweightingmaximum entropy

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Tsinghua Science and Technology
Volume 26 Issue 2,
April 2021
Pages 185-198
DOI: 10.26599/TST.2019.9010078
Cite this article:
Hu J, Pan Y, Li T, et al. TW-Co-MFC: Two-Level Weighted Collaborative Fuzzy Clustering Based on Maximum Entropy for Multi-View Data. Tsinghua Science and Technology, 2021, 26(2): 185-198. https://doi.org/10.26599/TST.2019.9010078

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Received: 08 December 2019
Accepted: 15 December 2019
Published: 24 July 2020
© The author(s) 2021.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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