Abstract
We propose a false-negative approach to approximate the set of frequent itemsets (FIs) over a sliding window. Existing approximate algorithms use an error parameter, ε, to control the accuracy of the mining result. However, the use of ε leads to a dilemma. A smaller ε gives a more accurate mining result but higher computational complexity, while increasing ε degrades the mining accuracy. We address this dilemma by introducing a progressively increasing minimum support function. When an itemset is retained in the window longer, we require its minimum support to approach the minimum support of an FI. Thus, the number of potential FIs to be maintained is greatly reduced. Our experiments show that our algorithm not only attains highly accurate mining results, but also runs significantly faster and consumes less memory than do existing algorithms for mining FIs over a sliding window.
This work is partially supported by RGC CERG under grant number HKUST6185/02E and HKUST6185/03E.
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© 2006 Springer-Verlag Berlin Heidelberg
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Cheng, J., Ke, Y., Ng, W. (2006). Maintaining Frequent Itemsets over High-Speed Data Streams. 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_53
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DOI: https://doi.org/10.1007/11731139_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33206-0
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