On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid

@article{Lenca2008OnSI,
  title={On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid},
  author={Philippe Lenca and Patrick Meyer and Beno{\^i}t Vaillant and St{\'e}phane Lallich},
  journal={European Journal of Operational Research},
  year={2008},
  volume={184},
  pages={610-626}
}
Data mining algorithms, especially those used for unsupervised learning, generate a large quantity of rules. In particular this applies to the Apriori family of algorithms for the determination of association rules. It is hence impossible for an expert in the field being mined to sustain these rules. To help carry out the task, many measures which evaluate the interestingness of rules have been developed. They make it possible to filter and sort automatically a set of rules with respect to… CONTINUE READING
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