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},
}
Downloads: 0
{"_id":"DHLE4XHXoWHcaQgFR","bibbaseid":"fu-lin-vasilakos-wang-anoverviewofrecentmultiviewclustering-2020","author_short":["Fu, L.","Lin, P.","Vasilakos, A. V.","Wang, S."],"bibdata":{"bibtype":"article","type":"article","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":[{"propositions":[],"lastnames":["Fu"],"firstnames":["Lele"],"suffixes":[]},{"propositions":[],"lastnames":["Lin"],"firstnames":["Pengfei"],"suffixes":[]},{"propositions":[],"lastnames":["Vasilakos"],"firstnames":["Athanasios","V."],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Shiping"],"suffixes":[]}],"month":"August","year":"2020","keywords":"Graph-based clustering, Machine learning, Multi-view clustering, Space learning, Unsupervised learning, clustering, multiview, multiview clustering","pages":"148–161","bibtex":"@article{fu_overview_2020,\n\ttitle = {An overview of recent multi-view clustering},\n\tvolume = {402},\n\tissn = {0925-2312},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0925231220303222},\n\tdoi = {10.1016/j.neucom.2020.02.104},\n\tabstract = {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.},\n\tlanguage = {en},\n\turldate = {2021-11-07},\n\tjournal = {Neurocomputing},\n\tauthor = {Fu, Lele and Lin, Pengfei and Vasilakos, Athanasios V. and Wang, Shiping},\n\tmonth = aug,\n\tyear = {2020},\n\tkeywords = {Graph-based clustering, Machine learning, Multi-view clustering, Space learning, Unsupervised learning, clustering, multiview, multiview clustering},\n\tpages = {148--161},\n}\n\n\n\n","author_short":["Fu, L.","Lin, P.","Vasilakos, A. V.","Wang, S."],"key":"fu_overview_2020","id":"fu_overview_2020","bibbaseid":"fu-lin-vasilakos-wang-anoverviewofrecentmultiviewclustering-2020","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0925231220303222"},"keyword":["Graph-based clustering","Machine learning","Multi-view clustering","Space learning","Unsupervised learning","clustering","multiview","multiview clustering"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["XJ7Gu6aiNbAiJAjbw","XvjRDbrMBW2XJY3p9","3C6BKwtiX883bctx4","5THezwiL4FyF8mm4G","RktFJE9cDa98BRLZF","qpxPuYKLChgB7ox6D","PfM5iniYHEthCfQDH","yanwtMpCcFaHzRwWb","iwKepCrWBps7ojhDx"],"keywords":["graph-based clustering","machine learning","multi-view clustering","space learning","unsupervised learning","clustering","multiview","multiview clustering"],"search_terms":["overview","recent","multi","view","clustering","fu","lin","vasilakos","wang"],"title":"An overview of recent multi-view clustering","year":2020}