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

Authors: Jiye Liang; Qianyu Shi; Xingwang Zhao

Addresses: Department of Computer Science, Taiyuan Normal University, China; Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, China ' Key Laboratory of Computational Intelligence and Chinese Information, Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, China ' Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, China

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.

Keywords: multi-view data; mixed data; k-prototypes clustering algorithm; ensemble clustering.

DOI: 10.1504/IJMISSP.2018.092938

International Journal of Machine Intelligence and Sensory Signal Processing, 2018 Vol.2 No.2, pp.97 - 120

Received: 27 Nov 2017
Accepted: 02 Dec 2017
Published online: 03 Jul 2018 *

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