Abstract
Association rule mining is one of important data mining problems. In this paper, a framework for efficiently calculating frequent itemsets in voluminous data is presented. The algorithm FIT [LR] is a practical implemention of the framework. A theoretical comparison between FIT and Eclat [ZPOW] is also explored. The analysis asserts that the performance of FIT is much more efficient than that of Eclat. Experimental results confirmed the assertion with data from [AS].
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Luo, J., Rajasekaran, S. (2005). A Framework for Mining Association Rules. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_71
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DOI: https://doi.org/10.1007/11554028_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28897-8
Online ISBN: 978-3-540-31997-9
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