Abstract
Mining frequent itemset using bit-vector representation approach is very efficient for small dense datasets, but highly inefficient for sparse datasets due to lack of any efficient bit-vector projection technique. In this paper we present a novel efficient bit-vector projection technique, for sparse and dense datasets. We also present a new frequent itemset mining algorithm Ramp ( Real Algorithm for Mining Patterns) using bit-vector representation approach and our bit-vector projection technique. The performance of the Ramp is compared with the current best frequent itemset mining algorithms. Different experimental results on sparse datasets show that mining frequent itemset using Ramp is faster than the current best algorithms.
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© 2006 Springer-Verlag Berlin Heidelberg
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Bashir, S., Baig, A.R. (2006). Ramp: High Performance Frequent Itemset Mining with Efficient Bit-Vector Projection Technique. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_59
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DOI: https://doi.org/10.1007/11731139_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33206-0
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