Schema Design for Causal Law Mining from Incomplete Database

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.