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Advances in Intelligent Information Systems pp 3–25Cite as

Nonredundant Generalized Rules and Their Impact in Classification

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 265))

Abstract

Association rules are commonly used in classification based on associations. These rules are made of conjunctions of attributes in the premise and a class attribute in conclusion. In this chapter, we are interested in understanding the impact of generalized association rules in classification processes. For that purpose, we investigate the use of generalized association rules, i.e., rules in which the conclusion is a disjunction of attributes. We propose a method which directly mines nonredundant generalized association rules, possibly with exceptions, by using the recent developments in condensed representations of pattern mining and hypergraph transversals computing. Then we study the impact of using such rules instead of classical ones for classification purposes. To that aim, we view generalized rules as rules with negations in the premise and possibly concluding on a negative class attribute. To study the impact of such rules, we feed the standard CMAR method with these rules and we compare the results with the use of classical ones.

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Authors and Affiliations

  1. GREYC, CNRS - UMR 6072, Université de Caen Basse-Normandie, F-14032, Caen cedex, France

    François Rioult, Bruno Zanuttini & Bruno Crémilleux

Authors
  1. François Rioult
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  2. Bruno Zanuttini
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  3. Bruno Crémilleux
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of North Carolina, N.C. 28223, Charlotte, USA

    Zbigniew W. Ras

  2. Department of Electronics, Computer & Information Technology, NC A &T State University, NC 27411, Greensboro, USA

    Li-Shiang Tsay

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Rioult, F., Zanuttini, B., Crémilleux, B. (2010). Nonredundant Generalized Rules and Their Impact in Classification. In: Ras, Z.W., Tsay, LS. (eds) Advances in Intelligent Information Systems. Studies in Computational Intelligence, vol 265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05183-8_1

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  • DOI: https://doi.org/10.1007/978-3-642-05183-8_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05182-1

  • Online ISBN: 978-3-642-05183-8

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