Latent Dirichlet Conditional Naive-Bayes Models for Privacy-Preservation Clustering

• DOI number:10.1109/ICCSN.2009.137
• Affiliation of Author(s):西南交通大学
• Journal:Pro of International Conference on Communication Software and Networks 2009 (ICCSN 2009)
• Place of Publication:Macao, China
• Key Words:Bayes methods approximation theory data mining data privacy expectation-maximisation algorithm pattern clustering distributed EM algorithm latent Dirichlet conditional Naive-Bayes model privacy-preservation clustering variational approximation inference
• Abstract:The paper introduces a model for privacy preservation clustering which can handle the problems of privacy preservation, distributed computing. First, the latent variables in latent Dirichlet conditional Naive-Bayes models (LDCNB)are redefined and some terminologies are defined. Second, Variational approximation inference for LD-CNBis stated in detail. Third, base on the variational approximation inference, we design a distributed EM algorithm for privacy preservation clustering. Finally, some datasets from UCI are chosen for experiment, Compared with the distributed k-means algorithm, the results show LD-CNB algorithm does work better and LD-CNB can work distributed,so LD-CNB can protect privacy information.
• Co-author:Zhishu Li,Yang Cheng.
• First Author:Hongjun Wang
• Indexed by:Academic papers
• Correspondence Author:Hongjun Wang
• Document Code:20094112365628
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• Issue:IEEE computer society 2009
• Page Number:684-688
• ISSN No.:978-0-7695-3522-7
• Translation or Not:no