Abstract
Association rule mining is a fundamental data mining task. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information.Furthermore, it is well-known that a large proportion of association rules generated are redundant. In previous works, these two problems have been addressed separately. In this paper, we address both of them at the same time by proposing an approximate algorithm named TNR for mining top-k non redundant association rules.
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Fournier-Viger, P., Tseng, V.S. (2012). Mining Top-K Non-redundant Association Rules. 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_4
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DOI: https://doi.org/10.1007/978-3-642-34624-8_4
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