Multi-View Adjacency-Constrained Hierarchical Clustering

Jie Yang, Chin-teng Lin

Abstract:

This paper explores the problem of multi-view clustering,
which aims to promote clustering performance with multiview
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.