Abstract
Certainty factor and lift are known evaluation measures of association rules. These measures, nevertheless, do not guarantee accurate evaluation of strength of dependence between rule’s constituents. In particular, even if there is a strongest possible positive or negative dependence between rule’s constituents X and Y, these measures may reach values quite close to the values characteristic for rule’s constituents independence. In this paper, we first re-examine both certainty factor and lift. Then, in order to better evaluate dependence between rule’s constituents, we offer and examine a new measure – a dependence factor. Unlike in the case of the certainty factor, when defining our measure, we take into account the fact that for a given rule X → Y, the minimal conditional probability of the occurrence of Y given X may be greater than 0, while its maximal possible value may less than 1. In the paper, a number of properties and relations of all investigated measures are derived.
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Kryszkiewicz, M. (2015). Dependence Factor for Association Rules. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9012. Springer, Cham. https://doi.org/10.1007/978-3-319-15705-4_14
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DOI: https://doi.org/10.1007/978-3-319-15705-4_14
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