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International Symposium on Intelligent Data Analysis

IDA 2013: Advances in Intelligent Data Analysis XII pp 68–79Cite as

Evaluation of Association Rule Quality Measures through Feature Extraction

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8207))

Abstract

The practical success of association rule mining depends heavily on the criterion to choose among the many rules often mined. Many rule quality measures exist in the literature. We propose a protocol to evaluate the evaluation measures themselves. For each association rule, we measure the improvement in accuracy that a commonly used predictor can obtain from an additional feature, constructed according to the exceptions to the rule. We select a reference set of rules that are helpful in this sense. Then, our evaluation method takes into account both how many of these helpful rules are found near the top rules for a given quality measure, and how near the top they are. We focus on seven association rule quality measures. Our experiments indicate that multiplicative improvement and (to a lesser extent) support and leverage (a.k.a. weighted relative accuracy) tend to obtain better results than the other measures.

This work has been partially supported by project BASMATI (TIN2011-27479-C04) of Programa Nacional de Investigación, Ministerio de Ciencia e Innovación (MICINN), Spain, and by the Pascal-2 Network of the European Union.

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Author information

Authors and Affiliations

  1. Departament de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain

    José L. Balcázar

  2. Advanced Information Technology Institute, Ghana-India Kofi Annan Centre of Excellence in ICT, Accra, Ghana

    Francis Dogbey

Authors
  1. José L. Balcázar
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  2. Francis Dogbey
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Editor information

Editors and Affiliations

  1. School of Information Systems, Computing and Mathematics, Brunel University, UB8 3PH, Uxbridge, Middlesex, UK

    Allan Tucker  & Stephen Swift  & 

  2. Faculty of Computer Science/IT, Ostfalia University of Applied Sciences, Am Exer 2, 38302, Wolfenbüttel, Germany

    Frank Höppner

  3. Faculty of Science, Department of Information and Computing Science, Buys Ballot Laboratory, Universiteit Utrecht, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Arno Siebes

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Balcázar, J.L., Dogbey, F. (2013). Evaluation of Association Rule Quality Measures through Feature Extraction. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-41398-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41397-1

  • Online ISBN: 978-3-642-41398-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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