  Hierarchical clustering recursively partitions data at an increasingly finer
granularity. In real-world applications, multi-view data have become
increasingly important. This raises a less investigated problem, i.e.,
multi-view hierarchical clustering, to better understand the hierarchical
structure of multi-view data. To this end, we propose a novel neural
network-based model, namely Contrastive Multi-view Hyperbolic Hierarchical
Clustering (CMHHC). It consists of three components, i.e., multi-view alignment
learning, aligned feature similarity learning, and continuous hyperbolic
hierarchical clustering. First, we align sample-level representations across
multiple views in a contrastive way to capture the view-invariance information.
Next, we utilize both the manifold and Euclidean similarities to improve the
metric property. Then, we embed the representations into a hyperbolic space and
optimize the hyperbolic embeddings via a continuous relaxation of hierarchical
clustering loss. Finally, a binary clustering tree is decoded from optimized
hyperbolic embeddings. Experimental results on five real-world datasets
demonstrate the effectiveness of the proposed method and its components.
