Recent Frequent Item Mining Algorithm in a Data Stream Based on Flexible Counter Windows

Yan Yang Guo; Gang Wang; Feng Mei Hou; Qing Ling Mei

doi:10.4028/www.scientific.net/AMM.385-386.1415

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 385-386Recent Frequent Item Mining Algorithm in a Data...

Recent Frequent Item Mining Algorithm in a Data Stream Based on Flexible Counter Windows

Abstract:

In the paper the author introduces FCW_MRFI, which is a streaming data frequent item mining algorithm based on variable window. The FCW_MRFI algorithm can mine frequent item in any window of recent streaming data, whose given length is L. Meanwhile, it divides recent streaming data into several windows of variable length according to m, which is the number of the counter array. This algorithm can achieve smaller query error in recent windows, and can minimize the maximum query error in the whole recent streaming data.

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Periodical:

Applied Mechanics and Materials (Volumes 385-386)

Pages:

1415-1418

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.385-386.1415

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Online since:

August 2013

Authors:

Yan Yang Guo, Gang Wang, Feng Mei Hou, Qing Ling Mei

Keywords:

Counter Array, Data Mining (DM), Most Recent Frequent Item, Streaming Data

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