Distributed and Parallelled EM Algorithm for Distributed Cluster Ensemble

• DOI number:10.1109/PACIIA.2008.346
• Affiliation of Author(s):西南交通大学
• Journal:2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application
• Place of Publication:Wuhan, China
• Key Words:data privacy data visualisation expectation-maximisation algorithm inference mechanisms parallel algorithms pattern clustering data visualization distributed cluster ensemble distributed computing knowledge reuse
• Abstract:The paper introduces base clusterings distributed cluster ensemble which can handle the problems of privacy preservation, distributed computing and knowledge reuse. First, the latent variables in latent Dirichlet location model for cluster ensemble (LDA-CE) are defined and some terminologies are defined. Second, Variational approximation inference for LDA-CE is stated in detail. Third, base on the variational approximation inference, we design a distributed and paralleled EM algorithm for cluster ensemble (DPEM). Finally, some datasets from UCI are chosen for experiment, Compared with cluster-based similarity partitioning algorithm (CSPA), hyper-graph partitioning algorithm(HGPA) and meta-clustering algorithm(MCLA), the results show DPEM algorithm does work better and DPEM can work distributed and paralleled, so DPEM can protect privacy information more and can save time.
• Co-author:Zhishu Li,Yang Cheng.
• First Author:Hongjun Wang
• Indexed by:Academic papers
• Correspondence Author:Hongjun Wang
• Document Code:20091412011813
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• Volume:Volume 2
• Issue:PACIIA 2008
• Translation or Not:no