Learning Local Components to Understand Large Bayesian Networks

Yifeng Zeng, Yanping Xiang, Jorge Cordero, Yujian Lin

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)


Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users (domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data.
Original languageEnglish
Title of host publicationIEEE International Conference on Data Mining (ICDM 2009)
PublisherIEEE Computer Society Press
Publication date2009
ISBN (Print)978-0-7695-3895-2
Publication statusPublished - 2009
EventIEEE International Conference on Data Mining (ICDM 2009) - Miami, Florida, United States
Duration: 6 Dec 20099 Jan 2010
Conference number: 9


ConferenceIEEE International Conference on Data Mining (ICDM 2009)
Country/TerritoryUnited States
CityMiami, Florida
SeriesProceedings of the 2009 Ninth IEEE International Conference on Data Mining


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