Multi-view clustering: A survey. Big Data Mining and Analytics, 1(2):83–107, June, 2018. Paper doi abstract bibtex In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views, while fusing these data. Multi-view Clustering (MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets. Overall, this paper serves as an introductory text and survey for multi-view clustering.
@article{noauthor_multi-view_2018,
title = {Multi-view clustering: {A} survey},
volume = {1},
issn = {2096-0654},
shorttitle = {Multi-view clustering},
url = {https://ieeexplore.ieee.org/document/8336846/},
doi = {10.26599/BDMA.2018.9020003},
abstract = {In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views, while fusing these data. Multi-view Clustering (MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets. Overall, this paper serves as an introductory text and survey for multi-view clustering.},
language = {en},
number = {2},
urldate = {2023-10-24},
journal = {Big Data Mining and Analytics},
month = jun,
year = {2018},
pages = {83--107},
}
Downloads: 0
{"_id":"yCfCt2g2fwc8AFzQN","bibbaseid":"anonymous-multiviewclusteringasurvey-2018","bibdata":{"bibtype":"article","type":"article","title":"Multi-view clustering: A survey","volume":"1","issn":"2096-0654","shorttitle":"Multi-view clustering","url":"https://ieeexplore.ieee.org/document/8336846/","doi":"10.26599/BDMA.2018.9020003","abstract":"In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views, while fusing these data. Multi-view Clustering (MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets. Overall, this paper serves as an introductory text and survey for multi-view clustering.","language":"en","number":"2","urldate":"2023-10-24","journal":"Big Data Mining and Analytics","month":"June","year":"2018","pages":"83–107","bibtex":"@article{noauthor_multi-view_2018,\n\ttitle = {Multi-view clustering: {A} survey},\n\tvolume = {1},\n\tissn = {2096-0654},\n\tshorttitle = {Multi-view clustering},\n\turl = {https://ieeexplore.ieee.org/document/8336846/},\n\tdoi = {10.26599/BDMA.2018.9020003},\n\tabstract = {In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views, while fusing these data. Multi-view Clustering (MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets. Overall, this paper serves as an introductory text and survey for multi-view clustering.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-10-24},\n\tjournal = {Big Data Mining and Analytics},\n\tmonth = jun,\n\tyear = {2018},\n\tpages = {83--107},\n}\n\n","key":"noauthor_multi-view_2018","id":"noauthor_multi-view_2018","bibbaseid":"anonymous-multiviewclusteringasurvey-2018","role":"","urls":{"Paper":"https://ieeexplore.ieee.org/document/8336846/"},"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/victorjhu","dataSources":["CmHEoydhafhbkXXt5"],"keywords":[],"search_terms":["multi","view","clustering","survey"],"title":"Multi-view clustering: A survey","year":2018}