Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Connectionist learning procedures
Artificial Intelligence
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Advances in knowledge discovery and data mining
A tutorial on learning with Bayesian networks
Learning in graphical models
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An Introduction to Algorithms for Inference in Belief Nets
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Profiling your customers using Bayesian networks
ACM SIGKDD Explorations Newsletter
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning Bayesian networks from incomplete databases
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Raising data for improved support in rule mining: How to raise and how far to raise
Intelligent Data Analysis
Explaining inferences in Bayesian networks
Applied Intelligence
Incorporating Metadata into Data Mining with Ontology
IEICE - Transactions on Information and Systems
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Some existing data mining methods, such as classification trees, neural networks and association rules, have the drawbacks that the user's prior knowledge cannot be easily specified and incorporated into the knowledge discovery process, and the rules mined from databases lack quantitative analyses. In this paper, we propose a belief networks method for rule mining, which takes the advantage of belief networks as the directed acyclic graph language and their function for numerical representation of probabilistic dependencies among the variables in the database, so that it can overcome the drawbacks. Since belief networks provide a natural representation for capturing causal relationship among a set of variables, our proposed method can mine more general correlation rules which can capture the relationship of more than two attribute variables. The potential application of the proposed method is demonstrated through the detailed case studies on benchmark databases.