Filtering Association Rules with Negations on the Basis of Their Confidence Boost

@inproceedings{Balczar2010FilteringAR,
  title={Filtering Association Rules with Negations on the Basis of Their Confidence Boost},
  author={Jos{\'e} L. Balc{\'a}zar and Cristina T{\^i}rnauca and Marta E. Zorrilla},
  booktitle={KDIR},
  year={2010}
}
We consider a recent proposal to filter association rules on the basis of their novelty: the confidence boost. We develop appropriate mathematical tools to understand it in the presence of negated attributes, and explore the effect of applying it to association rules with negations. We show that, in many cases, the notion of confidence boost allows us to obtain reasonably sized output consisting of intuitively interesting association rules with negations. 

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Min - ing educational data for patterns with negations and high confidence boost

J. L. Balcázar, C. Tı̂rnăucă, M. Zorrilla
2010
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Mining educational data for patterns with negations and high confidence boost. Accepted for TAMIDA’2010; available at: [http://personales.unican.es/balcazarjl

J. L. Balcázar, C. Tı̂rnăucă, M. Zorrilla
2010
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