Mining Approximate Frequent Itemsets over Data Streams

Na Su; Zhe Hui Wu; Ji Min Liu; Tai An Liu; Xin Jun An; Chang Qing Yan

doi:10.4028/www.scientific.net/AMM.685.536

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 685Mining Approximate Frequent Itemsets over Data...

Mining Approximate Frequent Itemsets over Data Streams

Abstract:

This paper proposes a method based on Lossy Counting to mine frequent itemsets. Logarithmic tilted time window is adopted to emphasize the importance of recent data. Multilayer count queue framework is used to avoid the counter overflowing and query top-K itemsets quickly using a index table.

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Periodical:

Applied Mechanics and Materials (Volume 685)

Pages:

536-539

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.685.536

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Online since:

October 2014

Authors:

Na Su*, Zhe Hui Wu, Ji Min Liu, Tai An Liu, Xin Jun An, Chang Qing Yan

Keywords:

Data Stream, Frequent Itemsets, Logarithmic Tilted Time Window

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