Ensemble clustering using factor graph. (February 2016)

• Record Type: Journal Article
• Authors: Huang, Dong; Lai, Jianhuang; Wang, Chang-Dong
• Is Part Of: Pattern recognition. Volume 50(2016:Feb.)
• Page Start: 131
• Page End: 142
• Publication Date: 2016-02
• Subjects: Ensemble clustering -- Factor graph -- Belief propagation -- Super-object -- Automatic cluster number estimate; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.08.015
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: In this paper, we propose a new ensemble clustering approach termed ensemble clustering using factor graph (ECFG). Compared to the existing approaches, our approach has three main advantages: (1) the cluster number is obtained automatically and need not to be specified in advance; (2) the reliability of each base clustering can be estimated in an unsupervised manner and exploited in the consensus process; (3) our approach is efficient for processing ensembles with large data sizes and large ensemble sizes. In this paper, we introduce the concept of super-object, which serves as a compact and adaptive representation for the ensemble data and significantly facilitates the computation. Through the probabilistic formulation, we cast the ensemble clustering problem into a binary linear programming (BLP) problem. The BLP problem is NP-hard. To solve this optimization problem, we propose an efficient solver based on factor graph. The constrained objective function is represented as a factor graph and the max-product belief propagation is utilized to generate the solution insensitive to initialization and converged to the neighborhood maximum. Extensive experiments are conducted on multiple real-world datasets, which demonstrate the effectiveness and efficiency of our approach against the state-of-the-art approaches. Abstract : Highlights: Introduce the super-object representation to facilitate the consensus process. Probabilistically formulate the ensemble clustering problem into a BLP problem. Propose an efficient solver for the BLP problem based on factor graph. The cluster number of the consensus clustering is estimated automatically. Our method achieves the state-of-the-art performance in effectiveness and efficiency.