An overview of recent multi-view clustering
- Publisher:
- Elsevier BV
- Publication Type:
- Journal Article
- Citation:
- Neurocomputing, 2020, 402, pp. 148-161
- Issue Date:
- 2020-08-18
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S0925231220303222-main.pdf | Published version | 1.55 MB |
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© 2020 Elsevier B.V. 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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