Maintaining frequent itemsets over high-speed data streams

19Citations
Citations of this article
19Readers
Mendeley users who have this article in their library.
Get full text

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 e 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. © Springer-Verlag Berlin Heidelberg 2006.

Cite

CITATION STYLE

APA

Cheng, J., Ke, Y., & Ng, W. (2006). Maintaining frequent itemsets over high-speed data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3918 LNAI, pp. 462–467). https://doi.org/10.1007/11731139_53

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free