Machine Learning

, Volume 55, Issue 2, pp 175–210

Inducing Multi-Level Association Rules from Multiple Relations

  • Francesca A. Lisi
  • Donato Malerba


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.

inductive logic programming description logics spatial data mining 

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Francesca A. Lisi
  • Donato Malerba

There are no affiliations available

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