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 AL-log which allows a unified treatment of both the relational and structural features of data. A generality order and a downward refinement operator for AL-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.
Although it has been presented as a system able to use is-a hierarchies, WARMR is not a system for mining multi-level association rules because it lacks of mechanisms for taxonomic reasoning REF .
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Machine Learning
Computer Science Mathematics