Learning Local Components to Understand Large Bayesian Networks

Yifeng Zeng, Yanping Xiang, Jorge Cordero H., Yujian Lin

    Research output: Contribution to conferencePaperpeer-review

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    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 interrelevant 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
    Number of pages6
    Publication statusPublished - Dec 2009
    Event9th IEEE International Conference on Data Mining (ICDM) - Miami Beach, United States
    Duration: 6 Dec 20099 Dec 2009
    Conference number: 9th


    Conference9th IEEE International Conference on Data Mining (ICDM)
    Country/TerritoryUnited States


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