Schema Design for Causal Law Mining from Incomplete Database

Authors:
Kazunori Matsumoto;Kazuo Hashimoto
Affiliations:
-;-
Venue:
DS '99 Proceedings of the Second International Conference on Discovery Science
Year:
1999

Citing 4
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Learning Bayesian networks with local structure

Learning in graphical models
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

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Abstract

The paper describes the causal law mining from an incomplete database. First we extend the definition of association rules in order to deal with uncertain attribute values in records. As Agrawal's well-know algorithm generates too many irrelevant association rules, a filtering technique based on minimal AIC principle is applied here. The graphic representation of association rules validated by a filter may have directed cycles. The authors propose a method to exclude useless rules with a stochastic test, and to construct Bayesian networks from the remaining rules. Finally, a schem for Causal Law Mining is proposed as an integration of the techniques described in the paper.