Abstract
It is a common issue that KDD processes may generate a large number of patterns depending on the algorithm used, and its parameters. It is hence impossible for an expert to sustain these patterns. This may be the case with the well-known Apriori algorithm. One of the methods used to cope with such an amount of output depends on the use of interestingness measures. Stating that selecting interesting rules also means using an adapted measure, we present an experimental study of the behaviour of 20 measures on 10 datasets. This study is compared to a previous analysis of formal and meaningful properties of the measures, by means of two clusterings. One of the goals of this study is to enhance our previous approach. Both approaches seem to be complementary and could be profitable for the problem of a user’s choice of a measure.
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Vaillant, B., Lenca, P., Lallich, S. (2004). A Clustering of Interestingness Measures. In: Suzuki, E., Arikawa, S. (eds) Discovery Science. DS 2004. Lecture Notes in Computer Science(), vol 3245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30214-8_23
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DOI: https://doi.org/10.1007/978-3-540-30214-8_23
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