Mining generalized association rules with fuzzy taxonomic structures

Abstract

Data mining is a key step of knowledge discovery in databases. Usually, Srikant and Agrawal's (1995) algorithm is used for mining generalized association rules at all levels of presumed exact taxonomic structures. However, in many real-world applications, the taxonomic structures may not be crisp but fuzzy. This paper focuses on the issue of mining generalized association rules with fuzzy taxonomic structures. Particular attention is paid to extending the notions of the degree of support, the degree of confidence and the R-interest measure. The computation of these degrees takes into account the fact that there exists a partial belonging of any two item sets in the taxonomy concerned. Finally, a simplified example is given to help illustrate the ideas.