Abstract
Intensity of inclination, an objective rule-interest measure, allows us to extract implications on databases without having to go through the step of transforming the initial set of attributes into binary attributes, thereby avoiding obtaining a prohibitive number of rules of little significance with many redundancies. This new kind of rule, ordinal association rules, reveals the overall behavior of the population and it is vital that this study be extended by exploring specific ordinal association rules in order to refine our analysis and to extract behaviors in sub-sets. This paper focuses on the mining of association rules based on extracted ordinal association rules in order to, on the one hand remove the discretization step of numeric attributes and the step of complete disjunctive coding, and on the other hand obtain a variable discretization of numeric attributes i.e. dependent on association of attributes. The study ends with an evaluation of an application to some banking data.
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Guillaume, S. (2003). Ordinal Association Rules towards Association Rules. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2003. Lecture Notes in Computer Science, vol 2737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45228-7_17
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DOI: https://doi.org/10.1007/978-3-540-45228-7_17
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