Multi-view Clustering and Multi-view Models

Over the years, the development of information technology applications, in particular, Artificial Intelligence has spurred the need to collect multi-view data on a large scale. Multi-view data, in general, is large, heterogeneous and uncertain, but also contains a lot of knowledge to mine and apply. Some of the single-view data clustering techniques have been improved to analyze multi-view data by extending the structure of the objective function or building associative models. Currently, multi-view clustering methods are quite rich and diverse, however, each method is usually only effective for a specific group of problems. In order to provide an overview of multi-view data clustering and select suitable methods for specific applications, in this chapter we will review some methods of multi-view clustering. Simultaneously, we also select a number of multi-view data clustering algorithms to present and analyze some potential knowledge extraction mechanisms in the data views. Some experimental results on the benchmark datasets are also analyzed to evaluate the performance and scalability of different multi-view clustering algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.