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Open Access

Multi-view Clustering: A Survey

School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.

∙ Hao Wang is currently a PhD candidate and shares first authorship.

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Abstract

In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views, while fusing these data. Multi-view Clustering (MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multi-view graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets. Overall, this paper serves as an introductory text and survey for multi-view clustering.

Keywords

data miningconditional functional dependencybig datadata quality

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Big Data Mining and Analytics
Volume 1 Issue 2,
June 2018
Pages 83-107
DOI: 10.26599/BDMA.2018.9020003
Cite this article:
Yang Y, Wang H. Multi-view Clustering: A Survey. Big Data Mining and Analytics, 2018, 1(2): 83-107. https://doi.org/10.26599/BDMA.2018.9020003

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Received: 02 August 2017
Accepted: 28 November 2017
Published: 12 April 2018
© The author(s) 2018
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