Multi-view data ensemble clustering: a cluster-level perspective

Abstract

Ensemble clustering has recently emerged a powerful clustering analysis technology for multi-view data. From the existing work, these techniques held great promise, but most of them are inefficient for large data. Some researchers have also proposed efficient ensemble clustering algorithms, but these algorithms devote to data objects with the same feature spaces, which are not satisfied for multi-view data. To overcome these deficiencies, an efficient ensemble clustering algorithm for multi-view mixed data is developed from the cluster-level perspective. Firstly, a set of clustering solutions are produced with the K-prototypes clustering algorithm on each view multiple times, respectively. Then, a cluster-cluster similarity matrix is constructed by considering all the clustering solutions. Next, the METIS algorithm is conduct meta-clustering based on the similarity matrix. After that, the final clustering results are obtained by applying majority voting to assign the objects to their corresponding clusters based on the meta-clustering. The corresponding time complexity of the proposed algorithm is analysed as well. Experimental results on several multi-view datasets demonstrated the superiority of our proposed algorithm.