On Attribute Dependency Detection using Rules Obtained from Tabular Data

Abstract

We have investigated rule generation under uncertain information and proposed the DIS-Apriori and the NIS-Apriori algorithms. Using such algorithms and the implemented software tools, we can handle rules. This paper considers detecting dependency between attributes based on the obtained rules. Intuitively, the sum of the support values of the obtained rules becomes the degree of dependency. Therefore, we try to find attributes of rules with high support values. Such attributes are candidates that derive dependency.