A new method for ranking discovered rules from data mining by DEA

Mehdi Toloo*, Babak Sohrabi, Soroosh Nalchigar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)


Data mining techniques, extracting patterns from large databases have become widespread in business. Using these techniques, various rules may be obtained and only a small number of these rules may be selected for implementation due, at least in part, to limitations of budget and resources. Evaluating and ranking the interestingness or usefulness of association rules is important in data mining. This paper proposes a new integrated data envelopment analysis (DEA) model which is able to find most efficient association rule by solving only one mixed integer linear programming (MILP). Then, utilizing this model, a new method for prioritizing association rules by considering multiple criteria is proposed. As an advantage, the proposed method is computationally more efficient than previous works. Using an example of market basket analysis, applicability of our DEA based method for measuring the efficiency of association rules with multiple criteria is illustrated.

Original languageEnglish
Pages (from-to)8503-8508
Number of pages6
JournalExpert Systems with Applications
Issue number4
Publication statusPublished - May 2009
Externally publishedYes


  • Association rule
  • Data envelopment analysis
  • Data mining
  • Interestingness

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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