Abstract
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 language | English |
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Title of host publication | IEEE International Conference on Data Mining (ICDM 2009) |
Publisher | IEEE Computer Society Press |
Publication date | 2009 |
Edition | 9 |
Pages | 1076-1081 |
ISBN (Print) | 978-0-7695-3895-2 |
DOIs | |
Publication status | Published - 2009 |
Event | IEEE International Conference on Data Mining (ICDM 2009) - Miami, Florida, United States Duration: 6 Dec 2009 → 9 Jan 2010 Conference number: 9 |
Conference
Conference | IEEE International Conference on Data Mining (ICDM 2009) |
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Number | 9 |
Country/Territory | United States |
City | Miami, Florida |
Period | 06/12/2009 → 09/01/2010 |
Series | Proceedings of the 2009 Ninth IEEE International Conference on Data Mining |
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ISSN | 1550-4786 |