An overview of recent multi-view clustering
With the widespread deployment of sensors and the Internet-of-Things, multi-view data has become more common and publicly available. Compared to traditional data that describes objects from single perspective, multi-view data is semantically richer, more useful, however more complex. Since tradition...
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| Published in | Neurocomputing (Amsterdam) Vol. 402; pp. 148 - 161 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
18.08.2020
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| Subjects | |
| Online Access | Get full text |
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| Abstract | With the widespread deployment of sensors and the Internet-of-Things, multi-view data has become more common and publicly available. Compared to traditional data that describes objects from single perspective, multi-view data is semantically richer, more useful, however more complex. Since traditional clustering algorithms cannot handle such data, multi-view clustering has become a research hotspot. In this paper, we review some of the latest multi-view clustering algorithms, which are reasonably divided into three categories. To evaluate their performance, we perform extensive experiments on seven real-world data sets. Three mainstream metrics are used, including clustering accuracy, normalized mutual information and purity. Based on the experimental results and a large number of literature reading, we also discuss existing problems in current multi-view clustering and point out possible research directions in the future. This research provides some insights for researchers in related fields and may further promote the development of multi-view clustering algorithms. |
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| AbstractList | With the widespread deployment of sensors and the Internet-of-Things, multi-view data has become more common and publicly available. Compared to traditional data that describes objects from single perspective, multi-view data is semantically richer, more useful, however more complex. Since traditional clustering algorithms cannot handle such data, multi-view clustering has become a research hotspot. In this paper, we review some of the latest multi-view clustering algorithms, which are reasonably divided into three categories. To evaluate their performance, we perform extensive experiments on seven real-world data sets. Three mainstream metrics are used, including clustering accuracy, normalized mutual information and purity. Based on the experimental results and a large number of literature reading, we also discuss existing problems in current multi-view clustering and point out possible research directions in the future. This research provides some insights for researchers in related fields and may further promote the development of multi-view clustering algorithms. |
| Author | Fu, Lele Vasilakos, Athanasios V. Lin, Pengfei Wang, Shiping |
| Author_xml | – sequence: 1 givenname: Lele surname: Fu fullname: Fu, Lele organization: College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China – sequence: 2 givenname: Pengfei surname: Lin fullname: Lin, Pengfei organization: College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China – sequence: 3 givenname: Athanasios V. surname: Vasilakos fullname: Vasilakos, Athanasios V. organization: College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China – sequence: 4 givenname: Shiping orcidid: 0000-0001-5195-9682 surname: Wang fullname: Wang, Shiping email: shipingwangphd@163.com organization: College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China |
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| Snippet | With the widespread deployment of sensors and the Internet-of-Things, multi-view data has become more common and publicly available. Compared to traditional... |
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| SubjectTerms | Graph-based clustering Machine learning Multi-view clustering Space learning Unsupervised learning |
| Title | An overview of recent multi-view clustering |
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