2. Benefits of multi-view clustering

different groups. Clustering plays an important role in mining the hidden patterns. However,

With the rapid development of Internet and communication technology (ICT), the accesses to extract data are dramatically extended. That is, data can be collected from multiple sources or multiple facets. In such setting, each datum is associated with much richer information, which results in the requirement that to mine the intrinsic and valuable patterns hidden in the data, it is a necessity to take full advantage of the information contained in multiple sources. This issue is formally referred to as multi-view learning. To be more specific, each view corresponds to one source of information. For example, web pages can be described by both the page-contents (one view) and the hyperlink information (another view). Besides, different facets of a datum can also be treated as different views. For instance, an image can be characterized by its shape,

Obviously, integrating the information contained in multiple views can bring great benefits for data clustering. The most straightforward way to utilize the information of all views is to concatenate the data features of each view together and then perform the traditional clustering methods such as k-means. However, such a method lacks the ability to distinguish the different significance of different views. That is, the important views and less important views are treated equally, which may degrade the ultimate performance severely. 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 impor-

tance to the clustering task. Formally, this approach is known as multi-view clustering.

objective function should be designed, followed by the new solving method.

As an emerging and effective paradigm in data mining and machine learning, multi-view clustering refers to the clustering of the same class of data samples with multi-view representations, either from various information sources or from different feature generators. It is clear that if the clustering method cannot cope appropriately with multi-views, these views may even degrade the performance of multi-view clustering. To make use of multi-view information to improve clustering results, there are two main challenges to overcome. The first one is how to naturally ensemble the multiple clustering results of all the views. The second one is how to learn the importance of different views to the clustering task. In addition, these two issues should be figured out simultaneously. Thus, to achieve these goals, new clustering

Multi-view clustering was first studied by Bickel and Scheffer [1] in 2004. They extended the classic k-means and expectation maximization (EM) clustering methods to the multi-view environment to deal with text data with two conditionally independent views. Based on this seminal work, a variety of multi-view clustering methods have been proposed over the past two decades [2–4]. 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. Besides, many other multi-

most of the existing clustering algorithms are designed for single-view data.

color, and location.

196 Recent Applications in Data Clustering

Compared with the clustering methods that are implemented on single-view data, multi-view clustering is expected to obtain more robust and novel partitioning results by exploiting the redundant and complementary information in different views [5], as stated in the following sections.
