Structured multi-view k-means clustering

Abstract

$K$-means is a very efficient clustering method and many multi-view $k$-means clustering methods have been proposed for multi-view clustering during the past decade. However, since $k$-means have trouble uncovering clusters of varying sizes and densities, these methods suffer from the same performance issues as $k$-means. Improving the clustering performance of multi-view $k$-means has become a challenging problem. In this paper, we propose a new multi-view $k$-means clustering method that is able to uncover clusters in arbitrary sizes and densities. The new method simultaneously performs three tasks, i.e., sparse connection probability matrices learning, prototypes aligning, and cluster structure learning. We evaluate the proposed new method by 5 benchmark datasets and compare it with 11 multi-view clustering methods. The experimental results on both synthetic and real-world experiments show the superiority of our proposed method.