research-article

Self-weighted Multi-view Fuzzy Clustering

Authors:

Yang YangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 4

Article No.: 48, Pages 1 - 17

https://doi.org/10.1145/3396238

Published: 22 June 2020 Publication History

Abstract

Since the data in each view may contain distinct information different from other views as well as has common information for all views in multi-view learning, many multi-view clustering methods have been designed to use these information (including the distinct information for each view and the common information for all views) to improve the clustering performance. However, previous multi-view clustering methods cannot effectively detect these information so that difficultly outputting reliable clustering models. In this article, we propose a fuzzy, sparse, and robust multi-view clustering method to consider all kinds of relations among the data (such as view importance, view stability, and view diversity), which can effectively extract both distinct information and common information as well as balance these two kinds of information. Moreover, we devise an alternating optimization algorithm to solve the resulting objective function as well as prove that our proposed algorithm achieves fast convergence. It is noteworthy that existing multi-view clustering methods only consider a part of the relations, and thus are a special case of our proposed framework. Experimental results on synthetic datasets and real datasets show that our proposed method outperforms the state-of-the-art clustering methods in terms of evaluation metrics of clustering such as clustering accuracy, normalized mutual information, purity, and adjusted rand index.

References

[1]

Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil. 2007. Multi-task feature learning. In Proceedings of the 19th International Conference on Neural Information Processing Systems. 41--48.

[2]

Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, and Joydeep Ghosh. 2005. Clustering with bregman divergences. Journal of Machine Learning Research 6, 10 (2005), 1705--1749.

Digital Library

[3]

Steffen Bickel and Tobias Scheffer. 2004. Multi-view clustering. In Proceedings of the 4th IEEE International Conference on Data Mining. 19--26.

[4]

Diogo P. P. Branco and Francisco De A. T. De Carvalho. 2017. Fuzzy clustering of multi-view relational data with pairwise constraints. In Proceedings of the IEEE International Conference on Fuzzy Systems. 1--6.

[5]

Xiaochun Cao, Changqing Zhang, Huazhu Fu, and Si Liu. 2015. Diversity-induced multi-view subspace clustering. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. 586--594.

[6]

Francisco De A. T. De Carvalho, Filipe M. De Melo, and Yves Lechevallier. 2015. A multi-view relational fuzzy c-medoid vectors clustering algorithm. Neurocomputing 163, C (2015), 115--123.

[7]

Rick Chartrand and Wotao Yin. 2008. Iteratively reweighted algorithms for compressive sensing. In Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. 3869--3872.

[8]

Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, and Karthik Sridharan. 2009. Multi-view clustering via canonical correlation analysis. In Proceedings of the 26th Annual International Conference on Machine Learning. 129--136.

Digital Library

[9]

Guillaume Cleuziou, Matthieu Exbrayat, Lionel Martin, and Jacques-Henri Sublemontier. 2009. CoFKM: A centralized method for multiple-view clustering. In Proceedings of the 2009 9th IEEE International Conference on Data Mining. 752--757.

Digital Library

[10]

Ingrid Daubechies, Ronald DeVore, Massimo Fornasier, and C. Sinan Güntürk. 2010. Iteratively reweighted least squares minimization for sparse recovery. Communications on Pure and Applied Mathematics 63, 1 (2010), 1--38.

[11]

John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Tushar Chandra. 2008. Efficient projections onto the l 1-ball for learning in high dimensions. In Proceedings of the 25th International Conference on Machine Learning. 272--279.

Digital Library

[12]

J. C. Dunn. 1974. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3, 3 (1974), 32--57.

[13]

Matthias E. Futschik and Bronwyn Carlisle. 2005. Noise-robust soft clustering of gene expression time-course data. Journal of Bioinformatics and Computational Biology 3, 4 (2005), 965--988.

[14]

Rongyao Hu, Xiaofeng Zhu, Yonghua Zhu, and Jiangzhang Gan. 2020. Robust SVM with adaptive graph learning. World Wide Web 23, 3 (2020), 1945--1968.

Digital Library

[15]

Zexuan Ji, Yong Xia, Qiang Chen, Quansen Sun, Deshen Xia, and David Dagan Feng. 2012. Fuzzy c-means clustering with weighted image patch for image segmentation. Applied Soft Computing 12, 6 (2012), 1659--1667.

Digital Library

[16]

Yizhang Jiang, Fu-Lai Chung, Shitong Wang, Zhaohong Deng, Jun Wang, and Pengjiang Qian. 2015. Collaborative fuzzy clustering from multiple weighted views. IEEE Transactions on Cybernetics 45, 4 (2015), 688--701.

[17]

S. R. Kannan, S. Ramathilagam, R. Devi, and E. Hines. 2012. Strong fuzzy c-means in medical image data analysis. Journal of Systems 8 Software 85, 11 (2012), 2425--2438.

Digital Library

[18]

Hans-Peter Kriegel, Peer Kröger, and Arthur Zimek. 2009. Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Transactions on Knowledge Discovery From Data 3, 1 (2009), 1.

Digital Library

[19]

Cong Lei and Xiaofeng Zhu. 2018. Unsupervised feature selection via local structure learning and sparse learning. Multimedia Tools and Applications 77, 22 (2018), 29605--29622.

Digital Library

[20]

Fei-Fei Li, Robert Fergus, and Pietro Perona. 2007. Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. Computer Vision and Image Understanding 106, 1 (2007), 59--70.

Digital Library

[21]

Yeqing Li, Feiping Nie, Heng Huang, and Junzhou Huang. 2015. Large-scale multi-view spectral clustering via bipartite graph. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2750--2756.

[22]

M. E. Nilsback and A. Zisserman. 2006. A visual vocabulary for flower classification. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1447--1454.

[23]

R. D. Pinzon-Morales, K. A. Baquero-Duarte, A. A. Orozco-Gutierrez, and V. H. Grisales-Palacio. 2011. Pattern recognition of surface EMG biological signals by means of hilbert spectrum and fuzzy clustering. Advances in Experimental Medicine 8 Biology 696 (2011), 201.

[24]

Sam T. Roweis and Lawrence K. Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 5500 (2000), 2323--2326.

[25]

Fumin Shen, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, Zhenmin Tang, and Heng Tao Shen. 2015. Hashing on nonlinear manifolds. IEEE Transactions on Image Processing 24, 6 (2015), 1839--1851.

Digital Library

[26]

Heng Tao Shen, Luchen Liu, Yang Yang, Xing Xu, Zi Huang, Fumin Shen, and Richang Hong. 2020. Exploiting subspace relation in semantic labels for cross-modal hashing. IEEE Transactions on Knowledge and Data Engineering (2020).

[27]

Jingkuan Song, Yuyu Guo, Lianli Gao, Xuelong Li, Alan Hanjalic, and Heng Tao Shen. 2018. From deterministic to generative: Multimodal stochastic RNNs for video captioning. IEEE Transactions on Neural Networks and Learning Systems 30, 10 (2019), 3047--3058.

[28]

Yangtao Wang and Lihui Chen. 2017. Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources. Expert Systems with Applications 72 (2017), 457--466.

Digital Library

[29]

Zhimin Wang, Qing Song, Yeng Chai Soh, and Kang Sim. 2013. An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation. Computer Vision 8 Image Understanding 117, 10 (2013), 1412--1420.

[30]

Rongkai Xia, Yan Pan, Lei Du, and Jian Yin. 2014. Robust multi-view spectral clustering via low-rank and sparse decomposition. In Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2149--2155.

[31]

Xuanli Lisa Xie and Gerardo Beni. 1991. A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis 8 Machine Intelligence 13, 8 (1991), 841--847.

Digital Library

[32]

Chang Xu, Dacheng Tao, and Chao Xu. 2013. A survey on multi-view learning. Arxiv Preprint Arxiv:1304.5634 (2013).

[33]

Jinglin Xu, Junwei Han, Kai Xiong, and Feiping Nie. 2016. Robust and sparse fuzzy k-means clustering. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2224--2230.

[34]

Rui Xu and Donald Wunsch. 2005. Survey of clustering algorithms. IEEE Transactions on Neural Networks 16, 3 (2005), 645--678.

Digital Library

[35]

Yoshihiro Yamanishi, Jean-Philippe Vert, and Minoru Kanehisa. 2004. Protein network inference from multiple genomic data: A supervised approach. In Proceedings of the International Conference on Intelligent Systems for Molecular Biology. 363--370.

Digital Library

[36]

Kai Yu, Shipeng Yu, and Volker Tresp. 2006. Soft clustering on graphs. In Proceedings of the 18th International Conference on Neural Information Processing Systems. 1553--1560.

[37]

Dao Qiang Zhang, Songcan Chen, Zhi Song Pan, and Ke Ren Tan. 2003. Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation. In Proceedings of the International Conference on Machine Learning and Cybernetics. 2189--2192.

[38]

Shichao Zhang, Xuelong Li, Ming Zong, Xiaofeng Zhu, and Ruili Wang. 2018. Efficient knn classification with different numbers of nearest neighbors. IEEE Transactions on Neural Networks and Learning Systems 29, 5 (2018), 1774--1785.

[39]

Jing Zhao, Xijiong Xie, Xin Xu, and Shiliang Sun. 2017. Multi-view learning overview: Recent progress and new challenges. Information Fusion 38 (2017), 43--54.

Digital Library

[40]

Wei Zheng, Xiaofeng Zhu, Guoqiu Wen, Yonghua Zhu, Hao Yu, and Jiangzhang Gan. 2020. Unsupervised feature selection by self-paced learning regularization. Pattern Recognition Letters 132 (2020), 4--11.

[41]

Wei Zheng, Xiaofeng Zhu, Yonghua Zhu, Rongyao Hu, and Cong Lei. 2018. Dynamic graph learning for spectral feature selection. Multimedia Tools and Applications 77, 22 (2018), 29739--29755.

Digital Library

[42]

Xiang Zhou, Fumin Shen, Li Liu, Wei Liu, Liqiang Nie, Yang Yang, and Heng Tao Shen. 2018. Graph convolutional network hashing. IEEE Transactions on Cybernetics 50, 4 (2020), 1460--1472.

[43]

Yimin Zhou, Ling Tian, Ce Zhu, Xin Jin, and Yu Sun. 2020. Video coding optimization for virtual reality 360-degree source. IEEE Journal of Selected Topics in Signal Processing 14, 1 (2020), 118--129.

[44]

Xiaofeng Zhu, Jiangzhang Gan, Guangquan Lu, Jiaye Li, and Shichao Zhang. 2020. Spectral clustering via half-quadratic optimization. World Wide Web 23, 3 (2020), 1969--1988.

Digital Library

[45]

Xiaofeng Zhu, Xuelong Li, Shichao Zhang, Zongben Xu, Litao Yu, and Can Wang. 2017. Graph PCA hashing for similarity search. IEEE Transactions on Multimedia 19, 9 (2017), 2033--2044.

Digital Library

[46]

Xiaofeng Zhu, Jianye Yang, Chengyuan Zhang, and Shichao Zhang. 2019. Efficient utilization of missing data in cost-sensitive learning. IEEE Transactions on Knowledge and Data Engineering (2019).

[47]

Xiaofeng Zhu, Shichao Zhang, Rongyao Hu, Wei He, Cong Lei, and Pengfei Zhu. 2019. One-step multi-view spectral clustering. IEEE Transactions on Knowledge and Data Engineering 31, 10 (2019), 2022--2034.

[48]

Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, and Yue Fang. 2019. Low-rank sparse subspace for spectral clustering. IEEE Transactions on Knowledge and Data Engineering 31, 8 (2019), 1532--1543.

Digital Library

[49]

Xiaofeng Zhu, Yonghua Zhu, and Wei Zheng. 2019. Spectral rotation for deep one-step clustering. Pattern Recognition (2019).

Cited By

Liu JShang LSu YNie WWen XLiu A(2024)Privacy-preserving Multi-source Cross-domain Recommendation Based on Knowledge GraphACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363970620:5(1-18)Online publication date: 5-Jan-2024
https://dl.acm.org/doi/10.1145/3639706
Cui JFu YHuang CWen J(2024)Low-Rank Graph Completion-Based Incomplete Multiview ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.322405835:6(8064-8074)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3224058
Hu DDong ZLiang KYu HWang SLiu X(2024)High-order Topology for Deep Single-Cell Multiview Fuzzy ClusteringIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.339974032:8(4448-4459)Online publication date: 1-Aug-2024
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Index Terms

Self-weighted Multi-view Fuzzy Clustering
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 4

August 2020

316 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3403605

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

Issue’s Table of Contents

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2020

Online AM: 07 May 2020

Accepted: 01 April 2020

Revised: 01 February 2020

Received: 01 October 2019

Published in TKDD Volume 14, Issue 4

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Guangxi “Bagui” Teams for Innovation and Research
China Key Research Program
Guangxi High Institutions Program of Introducing 100 High-Level Overseas Talents
Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing
Marsden Fund of New Zealand (MAU1721)
Research Fund of Guangxi Key Lab of Multisource Information Mining and Security
Project of Guangxi Science and Technology
Natural Science Foundation of China

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Cited By

Liu JShang LSu YNie WWen XLiu A(2024)Privacy-preserving Multi-source Cross-domain Recommendation Based on Knowledge GraphACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363970620:5(1-18)Online publication date: 5-Jan-2024
https://dl.acm.org/doi/10.1145/3639706
Cui JFu YHuang CWen J(2024)Low-Rank Graph Completion-Based Incomplete Multiview ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.322405835:6(8064-8074)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3224058
Hu DDong ZLiang KYu HWang SLiu X(2024)High-order Topology for Deep Single-Cell Multiview Fuzzy ClusteringIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.339974032:8(4448-4459)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2024.3399740
Dornaika FEl Hajjar S(2024)An End-to-End Approach for Graph-Based Multi-View Data ClusteringIEEE Transactions on Big Data10.1109/TBDATA.2024.337135710:5(644-654)Online publication date: Oct-2024
https://doi.org/10.1109/TBDATA.2024.3371357
Li YHu XZhu TLiu JLiu XLiu Z(2024)Discriminative Embedded Multi-view Fuzzy C-Means Clustering for Feature-redundant and Incomplete DataInformation Sciences10.1016/j.ins.2024.120830(120830)Online publication date: Jun-2024
https://doi.org/10.1016/j.ins.2024.120830
Zhang CChen LShi ZDing W(2024)Latent information-guided one-step multi-view fuzzy clustering based on cross-view anchor graphInformation Fusion10.1016/j.inffus.2023.102025102(102025)Online publication date: Feb-2024
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Haris MYusoff YMohd Zain AKhattak AHussain S(2024)Breaking down multi-view clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.107857132:COnline publication date: 1-Jun-2024
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Liu ZLetchmunan S(2023)Enhanced Fuzzy Clustering for Incomplete Instance with Evidence CombinationACM Transactions on Knowledge Discovery from Data10.1145/363806118:3(1-20)Online publication date: 19-Dec-2023
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Wang SLan HPeng YPeng Z(2023)Consolidating Industrial Small Files Using Robust Graph ClusteringIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.319535010:5(2814-2831)Online publication date: 1-Sep-2023
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Li ALi YShao YLiu B(2023)Multi-View Scholar Clustering With Dynamic Interest TrackingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.324822135:9(9671-9684)Online publication date: 1-Sep-2023
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