Distributed Latent Dirichlet Allocation for Objects-Distributed Cluster ensemble

• DOI number:10.1109/NLPKE.2008.4906792
• Affiliation of Author(s):西南交通大学
• Journal:2008 International Conference on Natural Language Processing and Knowledge Engineering
• Place of Publication:Beijing, China
• Key Words:Markov processes Monte Carlo methods approximation theory data privacy distributed processing pattern clustering Markov chain Monte Carlo approximation inference UCI distributed computing distributed latent Dirichlet allocation
• Abstract:The paper introduces the model of distributed latent Dirichlet location (D-LDA) for objects-distributed cluster ensemble which can handle the problems of privacy preser-vation,distributed computing and knowledge reuse. First, the latent variables in D-LDA and some terminologies are de ned for cluster ensemble. Second, Markov chain Monte Carlo(MCMC)approximation inference for D-LDA is stated in detail. Third, some datasets from UCI are chosen for experiment, Compared with cluster-based similarity par-titioning algorithm (CSPA),hyper-graph partitioning algo-rithm(HGPA) and meta-clustering algorithm(MCLA), the results show D-LDA does work better, furthermore the out-puts of D-LDA, as a soft cluster model, can not only cluster the data points but also show the structure of data points.
• Co-author:Zhishu Li,Yang Cheng.
• First Author:Hongjun Wang
• Indexed by:Academic papers
• Correspondence Author:Hongjun Wang
• Document Code:20093012206323
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• ISSN No.:978-1-4244-2779-6
• Translation or Not:no