Abstract
The disadvantages of apriori algorithm are firstly discussed. Then, a new measure of kendall-τ is proposed and treated as an interest threshold. Furthermore, an improved Apriori algorithm called K-apriori is proposed based on kendall-τ correlation coefficient. It not only can accurately find the relations between different products in transaction databases and reduce the useless rules but also can generate synchronous positive rules, contrary positive rules and negative rules. Experiment has been carried out to verify the effectiveness of the algorithm. The result shows that the algorithm is effective at discovering the association rules in a sales management system.
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Zeng, A., Huang, Y. (2012). An Association Rules Algorithm Based on Kendall-τ . In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_22
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DOI: https://doi.org/10.1007/978-3-642-24553-4_22
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