Abstract
The goal of association mining is to find potentially interesting rules in large repositories of data. Unfortunately using a minimum support threshold, a standard practice to improve the association mining processing complexity, can allow some of these rules to remain hidden. This occurs because not all rules which have high confidence have a high support count. Various methods have been proposed to find these low support rules, but the resulting increase in complexity can be prohibitively expensive. In this paper, we propose a novel targeted association mining approach to rare rule mining using the itemset tree data structure (aka TRARM-RelSup). This algorithm combines the efficiency of targeted association mining querying with the capabilities of rare rule mining; this results in discovering a more focused, standard and rare rules for the user, while keeping the complexity manageable.
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Lavergne, J., Benton, R., Raghavan, V.V. (2012). TRARM-RelSup: Targeted Rare Association Rule Mining Using Itemset Trees and the Relative Support Measure. In: Chen, L., Felfernig, A., Liu, J., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2012. Lecture Notes in Computer Science(), vol 7661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34624-8_7
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DOI: https://doi.org/10.1007/978-3-642-34624-8_7
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