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Introducing a Rule Importance Measure

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Transactions on Rough Sets V

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 4100))

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Abstract

Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, interesting and important. We introduce a rough set based Rule Importance Measure to select the most important rules. We use ROSETTA software to generate multiple reducts. Apriori association rule algorithm is then applied to generate rule sets for each data set based on each reduct. Some rules are generated more frequently than the others among the total rule sets. We consider such rules as more important. We define rule importance as the frequency of an association rule generated across all the rule sets. Rule importance is different from either rule interestingness measures or rule quality measures because of their application tasks, the processes where the measures are applied and the contents they measure. The experimental results from an artificial data set, UCI machine learning datasets and an actual geriatric care medical data set show that our method reduces the computational cost for rule generation and provides an effective measure of how important is a rule.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

    Jiye Li

  2. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, B3H 1W5, Canada

    Nick Cercone

Authors
  1. Jiye Li
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  2. Nick Cercone
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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Li, J., Cercone, N. (2006). Introducing a Rule Importance Measure. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets V. Lecture Notes in Computer Science, vol 4100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11847465_8

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  • DOI: https://doi.org/10.1007/11847465_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-39382-5

  • Online ISBN: 978-3-540-39383-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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