Abstract
The development of effective interestingness measures that help in interpretation and evaluation of the discovered knowledge is an active research area in data mining and machine learning. In this paper, we consider a new Bayesian confirmation measure for ”if..., then...” rules proposed in [4]. We analyze this measure, called Z, with respect to valuable property M of monotonic dependency on the number of objects in the dataset satisfying or not the premise or the conclusion of the rule. The obtained results unveil interesting relationship between Z measure and two other simple and commonly used measures of rule support and anti-support, which leads to efficiency gains while searching for the best rules.
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Greco, S., Słowiński, R., Szczȩch, I. (2008). Assessing the Quality of Rules with a New Monotonic Interestingness Measure Z . In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_54
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DOI: https://doi.org/10.1007/978-3-540-69731-2_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69572-1
Online ISBN: 978-3-540-69731-2
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