Inducing Multi-Level Association Rules from Multiple Relations

摘要

Recently there has been growing interest both to extend ILP to description logics and to apply it to knowledge discovery in databases. In this paper we present a novel approach to association rule mining which deals with multiple levels of description granularity. It relies on the hybrid language \(\mathcal{A}\mathcal{L}\)-log which allows a unified treatment of both the relational and structural features of data. A generality order and a downward refinement operator for \(\mathcal{A}\mathcal{L}\)-log pattern spaces is defined on the basis of query subsumption. This framework has been implemented in SPADA, an ILP system for mining multi-level association rules from spatial data. As an illustrative example, we report experimental results obtained by running the new version of SPADA on geo-referenced census data of Manchester Stockport.