  This paper focuses on the multi-view clustering, which aims to promote
clustering results with multi-view data. Usually, most existing works suffer
from the issues of parameter selection and high computational complexity. To
overcome these limitations, we propose a Multi-view Hierarchical Clustering
(MHC), which partitions multi-view data recursively at multiple levels of
granularity. Specifically, MHC consists of two important components: the cosine
distance integration (CDI) and the nearest neighbor agglomeration (NNA). The
CDI can explore the underlying complementary information of multi-view data so
as to learn an essential distance matrix, which is utilized in NNA to obtain
the clustering results. Significantly, the proposed MHC can be easily and
effectively employed in real-world applications without parameter selection.
Experiments on nine benchmark datasets illustrate the superiority of our method
comparing to several state-of-the-art multi-view clustering methods.
