Abstract
Many real-world datasets can be naturally described by multiple views. Due to this, multi-view learning has drawn much attention from both academia and industry. Compared to single-view learning, multi-view learning has demonstrated plenty of advantages. Clustering has long been serving as a critical technique in data mining and machine learning. Recently, multi-view clustering has achieved great success in various applications. To provide a comprehensive review of the typical multi-view clustering methods and their corresponding recent developments, this chapter summarizes five kinds of popular clustering methods and their multi-view learning versions, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning. These clustering methods are the most widely employed algorithms for single-view data, and lots of efforts have been devoted to extending them for multi-view clustering. Besides, many other multi-view clustering methods can be unified into the frameworks of these five methods. To promote further research and development of multi-view clustering, some popular and open datasets are summarized in two categories. Furthermore, several open issues that deserve more exploration are pointed out in the end.
Highlights
Clustering is one of the most critical unsupervised learning techniques, which has been widely applied for data analysis, such as social network analysis, gene expression analysis, heterogeneous data analysis, and market analysis
Since covering all the proposed methods in one chapter is hard, to provide a comprehensive review of the typical multi-view clustering methods and their corresponding recent developments, we summarize five kinds of popular clustering methods and their multiview learning versions, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning
We provide a comprehensive review of the typical multi-view clustering methods and their corresponding recent developments by focusing on five most typical and popular clustering methods, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning
Summary
Clustering is one of the most critical unsupervised learning techniques, which has been widely applied for data analysis, such as social network analysis, gene expression analysis, heterogeneous data analysis, and market analysis. To take better advantage of the multi-view information, the ideal approach is to simultaneously perform the clustering using each view of data features and integrate their results based on their importance to the clustering task. Since covering all the proposed methods in one chapter is hard, to provide a comprehensive review of the typical multi-view clustering methods and their corresponding recent developments, we summarize five kinds of popular clustering methods and their multiview learning versions, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning This is based on the consideration that these clustering methods are the most widely employed algorithms for single-view data, and lots of efforts have been devoted to extending them for multi-view clustering.
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