Abstract
Association rules is a descriptive data mining technique, that has acquired major interest in the last years, with applications in several areas, such as: electronic and traditional commerce, securities, health care and geo-processing. This technique allows identifying intra-transactions patterns in a database. An association rule describes how much the presence of a set of attributes in a database’s record implicates in the presence of other distinct set of attributes in the same record. The possibility of discovering all the existent associations in a database transaction is the most relevant aspect of the technique. However, this characteristic determines the generation of a large number of rules, hindering the capacity of a human user in analyzing and interpreting the extracted knowledge. The use of rule measures is important to analyze the knowledge. In this context we investigate, firstly, the intensity of several objective measures to act as filters for rule sets. Next, we analyze how the combination of these measures can be used to identify the more interesting rules. Finally, we apply the proposed technique to a rule set, to illustrate its use in the post-processing phase.
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Melanda, E.A., Rezende, S.O. (2004). Combining Quality Measures to Identify Interesting Association Rules. In: Lemaître, C., Reyes, C.A., González, J.A. (eds) Advances in Artificial Intelligence – IBERAMIA 2004. IBERAMIA 2004. Lecture Notes in Computer Science(), vol 3315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30498-2_44
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DOI: https://doi.org/10.1007/978-3-540-30498-2_44
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