Abstract
We examine the complexity of Depth First and FP-growth implementations of Apriori, two of the fastest known data mining algorithms to find frequent itemsets in large databases. We describe the algorithms in a similar style, derive theoretical formulas, and provide experiments on both synthetic and real life data to illustrate the theory.
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© 2003 Springer-Verlag Berlin Heidelberg
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Kosters, W.A., Pijls, W., Popova, V. (2003). Complexity Analysis of Depth First and FP-Growth Implementations of APRIORI. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_25
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DOI: https://doi.org/10.1007/3-540-45065-3_25
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Online ISBN: 978-3-540-45065-8
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