6. Conclusion

Multi-view clustering has demonstrated variety of real-world applications, such as community detection in social networks, image annotation in computer vision, cross-domain user modeling in recommendation systems, and protein interaction analysis in bioinformatics. This chapter provides 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. The basic forms of these five clustering methods are introduced in detail, followed by a substantial overview of their recent developments. Several open datasets and open issues are discussed in the end, which deserves more attention to facilitate the future research of multi-view clustering.

In the field of multi-view clustering, there are many algorithms whose source codes are exposed by their authors. For example, the co-training<sup>1</sup> and co-regularization<sup>2</sup> methods of classical multi-view spectral clustering are open in GitHub with MATLAB. The variants MSE<sup>3</sup> and AMGL<sup>4</sup> are also implemented by MATLAB.
