An overview of recent multi-view clustering

• Published in: Neurocomputing (Amsterdam) Vol. 402; pp. 148 - 161
• Main Authors: Fu, Lele, Lin, Pengfei, Vasilakos, Athanasios V., Wang, Shiping
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 18.08.2020
• Subjects: Graph-based clustering; Machine learning; Multi-view clustering; Space learning; Unsupervised learning; Multi-view clustering; Unsupervised learning; Machine learning; Graph-based clustering; Space learning
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2020.02.104

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.