Detection of Fuzzy Association Rules by Fuzzy Transforms

Abstract EN

We present a new method based on the use of fuzzy transforms for detecting coarse-grained association rules in the datasets.

The fuzzy association rules are represented in the form of linguistic expressions and we introduce a pre-processing phase to determine the optimal fuzzy partition of the domains of the quantitative attributes.

In the extraction of the fuzzy association rules we use the AprioriGen algorithm and a confidence index calculated via the inverse fuzzy transform.

Our method is applied to datasets of the 2001 census database of the district of Naples (Italy); the results show that the extracted fuzzy association rules provide a correct coarse-grained view of the data association rule set.