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2008
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Verifying and Mining Frequent Patterns from Large Windows over Data Streams

11 years 1 months ago
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
Mining frequent itemsets from data streams has proved to be very difficult because of computational complexity and the need for real-time response. In this paper, we introduce a novel verification algorithm which we then use to improve the performance of monitoring and mining tasks for association rules. Thus, we propose a frequent itemset mining method for sliding windows, which is faster than the state-of-the-art methods--in fact, its running time that is nearly constant with respect to the window size entails the mining of much larger windows than it was possible before. The performance of other frequent itemset mining methods (including those on static data) can be improved likewise, by replacing their counting methods (e.g., those using hash trees) by our verification algorithm.
Barzan Mozafari, Hetal Thakkar, Carlo Zaniolo
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2008
Where ICDE
Authors Barzan Mozafari, Hetal Thakkar, Carlo Zaniolo
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