A multi-view clustering framework via integrating K-means and graph-cut

Abstract

Graph-cut and K-means are two classical clustering methods, which are used by most of existing clustering methods. First, a multi-view clustering framework via K-means and graph is proposed and a efficient clustering method is developed based on this framework. Our method calculates the discrete cluster assignment matrix directly and includes the auto-weighted strategy to consider the different contributions of views. Additionally, by generating the distance matrices for views with the corresponding graphs, our method fits both Gaussian and non-Gaussian distributed datasets. Moreover, the proposed algorithm is able to obtain robust results because it does not need initializing and computing cluster centroids. Extensive experiments on a synthetic dataset and four real-world datasets indicate the effectiveness of our method.