Mining Multi-Level Associations with Fuzzy Hierarchies
Rafal A. Angryk, Frederick E. Petry · 2005
In this paper we investigate application of fuzzy concept hierarchies to mining multi-level knowledge from large datasets via a well-known attribute-oriented induction approach (Han and Kamber, 2000). We analyze in detail the original process of fuzzy hierarchical induction and extend it with two new characteristics which improve applicability of the original approach to scientific data mining. These are a consistency of our fuzzy induction model, and an approximate drilling-down technique allowing a user to retrieve estimated explanations of the generated abstract concept. An application to discovery of multi-level association rules from environmental data stored in a toxic release inventory is presented