Ensemble clustering using factor graph. (February 2016) - Huang, Dong; Lai, Jianhuang; Wang, Chang-Dong
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.