An overview of recent multi-view clustering. Fu, L., Lin, P., Vasilakos, A. V., & Wang, S. Neurocomputing, 402:148–161, August, 2020. ![An overview of recent multi-view clustering [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{fu_overview_2020,
title = {An overview of recent multi-view clustering},
volume = {402},
issn = {0925-2312},
url = {https://www.sciencedirect.com/science/article/pii/S0925231220303222},
doi = {10.1016/j.neucom.2020.02.104},
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.},
language = {en},
urldate = {2021-11-07},
journal = {Neurocomputing},
author = {Fu, Lele and Lin, Pengfei and Vasilakos, Athanasios V. and Wang, Shiping},
month = aug,
year = {2020},
keywords = {Graph-based clustering, Machine learning, Multi-view clustering, Space learning, Unsupervised learning, clustering, multiview, multiview clustering},
pages = {148--161},
}
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