Abstract
Data-mining methods have the drawbacks to generate a very large number of rules, sometimes obvious, useless or not very interesting to the user. In this paper we propose a new approach to find unexpected rules from a set of discovered association rules. This technique is characterized by analyzing the discovered association rules using the user’s existing knowledge about the domain represented by a fuzzy domain ontology and then ranking the discovered rules according to the conceptual distance of the rule.
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Maamri, R., Hamani, M.s. (2011). Conceptual Distance for Association Rules Post-processing. In: Bellatreche, L., Mota Pinto, F. (eds) Model and Data Engineering. MEDI 2011. Lecture Notes in Computer Science, vol 6918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24443-8_18
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DOI: https://doi.org/10.1007/978-3-642-24443-8_18
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