Abstract
There were some traditional algorithms for mining global frequent itemsets. Most of them adopted Apriori-like algorithm frameworks. This resulted a lot of candidate itemsets, frequent database scans and heavy communication traffic. To solve these problems, this paper proposes a fast algorithm for mining global frequent itemsets, namely the FMGFI algorithm. It can easily get the global frequency for any itemsets from the local FP-tree and require far less communication traffic by the searching strategies of top-down and bottom-up. It effectively reduces existing problems of most algorithms for mining global frequent itemsets. Theoretical analysis and experimental results suggest that the FMGFI algorithm is fast and effective.
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© 2006 Springer-Verlag Berlin Heidelberg
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He, B., Wang, Y., Yang, W., Chen, Y. (2006). Fast Algorithm for Mining Global Frequent Itemsets Based on Distributed Database. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_60
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DOI: https://doi.org/10.1007/11795131_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
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