Multi-View Adjacency-Constrained Hierarchical Clustering

This paper explores the problem of multi-view clustering, which aims to promote clustering performance with multi-view data. The majority of existing methods have problems with parameter adjustment and high computational complexity. Moreover, in the past, there have been few works based on hierarchical clustering to learn the granular information of multiple views. To overcome these limitations, we propose a simple but efficient framework: Multi-view adjacency-Constrained Hierarchical Clustering (MCHC). Specifically, MCHC mainly consists of three parts: including the Fusion Distance matrices with Extreme Weights (FDEW); adjacency-Constrained Nearest Neighbor Clustering (CNNC); and the internal evaluation Index based on Rawls' Max-Min criterion (MMI). FDEW aims to learn a fusion distance matrix set, which not only uses complementary information among multiple views, but exploits the information from each single view. CNNC is utilized to generate multiple partitions based on FDEW, and MMI is designed for choosing the best one from the multiple partitions. In addition, we propose a parameter-free version of MCHC (MCHC-PF). Without any parameter selection, MCHC-PF can give partitions at different granularity levels with a low time complexity. Comprehensive experiments tested on eight real-world datasets validate the superiority of the proposed methods compared with the 13 current state-of-the-art methods.