Using classification to evaluate the output of confidence-based association rule mining

Stefan Mutter, Mark Hall, Eibe Frank

Research output: Contribution to journalConference article

26 Citations (Scopus)

Abstract

Association rule mining is a data mining technique that reveals interesting relationships in a database. Existing approaches employ different parameters to search for interesting rules. This fact and the large number of rules make it difficult to compare the output of confidence-based association rule miners. This paper explores the use of classification performance as a metric for evaluating their output. Previous work on forming classifiers from association rules has focussed on accurate classification, whereas we concentrate on using the properties of the resulting classifiers as a basis for comparing confidence-based association rule learners. Therefore, we present experimental results on 12 UCI datasets showing that the quality of small rule sets generated by Apriori can be improved by using the predictive Apriori algorithm. We also show that CBA, the standard method for classification using association rules, is generally inferior to standard rule learners concerning both running time and size of rule sets.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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title = "Using classification to evaluate the output of confidence-based association rule mining",
abstract = "Association rule mining is a data mining technique that reveals interesting relationships in a database. Existing approaches employ different parameters to search for interesting rules. This fact and the large number of rules make it difficult to compare the output of confidence-based association rule miners. This paper explores the use of classification performance as a metric for evaluating their output. Previous work on forming classifiers from association rules has focussed on accurate classification, whereas we concentrate on using the properties of the resulting classifiers as a basis for comparing confidence-based association rule learners. Therefore, we present experimental results on 12 UCI datasets showing that the quality of small rule sets generated by Apriori can be improved by using the predictive Apriori algorithm. We also show that CBA, the standard method for classification using association rules, is generally inferior to standard rule learners concerning both running time and size of rule sets.",
author = "Stefan Mutter and Mark Hall and Eibe Frank",
year = "2004",
month = "12",
day = "1",
language = "English",
volume = "3339",
pages = "538--549",
journal = "AI 2004: Advances in Artificial Intelligence",
issn = "1476-5586",
publisher = "Springer Verlag",

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Using classification to evaluate the output of confidence-based association rule mining. / Mutter, Stefan; Hall, Mark; Frank, Eibe.

In: AI 2004: Advances in Artificial Intelligence, Vol. 3339, 01.12.2004, p. 538-549.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Using classification to evaluate the output of confidence-based association rule mining

AU - Mutter, Stefan

AU - Hall, Mark

AU - Frank, Eibe

PY - 2004/12/1

Y1 - 2004/12/1

N2 - Association rule mining is a data mining technique that reveals interesting relationships in a database. Existing approaches employ different parameters to search for interesting rules. This fact and the large number of rules make it difficult to compare the output of confidence-based association rule miners. This paper explores the use of classification performance as a metric for evaluating their output. Previous work on forming classifiers from association rules has focussed on accurate classification, whereas we concentrate on using the properties of the resulting classifiers as a basis for comparing confidence-based association rule learners. Therefore, we present experimental results on 12 UCI datasets showing that the quality of small rule sets generated by Apriori can be improved by using the predictive Apriori algorithm. We also show that CBA, the standard method for classification using association rules, is generally inferior to standard rule learners concerning both running time and size of rule sets.

AB - Association rule mining is a data mining technique that reveals interesting relationships in a database. Existing approaches employ different parameters to search for interesting rules. This fact and the large number of rules make it difficult to compare the output of confidence-based association rule miners. This paper explores the use of classification performance as a metric for evaluating their output. Previous work on forming classifiers from association rules has focussed on accurate classification, whereas we concentrate on using the properties of the resulting classifiers as a basis for comparing confidence-based association rule learners. Therefore, we present experimental results on 12 UCI datasets showing that the quality of small rule sets generated by Apriori can be improved by using the predictive Apriori algorithm. We also show that CBA, the standard method for classification using association rules, is generally inferior to standard rule learners concerning both running time and size of rule sets.

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M3 - Conference article

VL - 3339

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EP - 549

JO - AI 2004: Advances in Artificial Intelligence

T2 - AI 2004: Advances in Artificial Intelligence

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SN - 1476-5586

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