Comparing Expert and Metric-Based Assessments of Association Rule Interestingness

@inproceedings{Bazaldua2014ComparingEA,
  title={Comparing Expert and Metric-Based Assessments of Association Rule Interestingness},
  author={Diego Luna Bazaldua and Ryan Shaun Joazeiro de Baker and Maria Ofelia San Pedro},
  booktitle={EDM},
  year={2014}
}
In association rule mining, interestingness refers to metrics that are applied to select association rules, beyond support and confidence. For example, Merceron & Yacef (2008) recommend that researchers use a combination of lift and cosine to select association rules, after first filtering out rules with low support and confidence. However, the empirical basis for considering these specific metrics to be evidence of interestingness is rather weak. In this study, we examine these metrics by… CONTINUE READING