Correlation-based Refinement of Rules with Numerical Attributes

Melo, André ; Theobald, Martin ; Völker, Johanna

Document Type: Conference or workshop publication
Year of publication: 2014
Book title: Proceedings of the twenty-seventh International Conference of the Florida Artificial Intelligence Research Society (FLAIRS) : May 21 - 23, 2014 Pensacola Beach, Florida, USA
Page range: 345-350
Date of the conference: 21.05.2014
Publisher: Eberle, William
Place of publication: Palo Alto, Calif.
Publishing house: AAAI Press
ISBN: 978-1-57735-658-5
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: Learning rules is a common way of extracting useful information from knowledge or data bases. Many of such data sets contain numerical attributes. However, approaches like ILP or association rule mining are optimized for data with categorical values, and considering numerical attributes is expensive. In this paper, we present an extension to top-down ILP algorithms such as FOIL, which enables an efficient discovery of rules from data with both numerical and categorical attributes. Our approach comprises a preprocessing phase for computing the correlations between numerical and categorical attributes, as well as an extension to the ILP refinement step, which enables us to detect interesting candidate rules and to suggest refinements with relevant attribute combinations. We report on experiments with U.S. Census data, Freebase and DBpedia, and show that our approach helps to efficiently discover rules with numerical intervals.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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