Incremental updates of closed frequent itemsets over continuous data streams

Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window....

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Bibliographic Details
Published in:Expert systems with applications 2009-03, Vol.36 (2), p.2451-2458
Main Authors: Li, Hua-Fu, Ho, Chin-Chuan, Lee, Suh-Yin
Format: Article
Language:English
Subjects:
Closed frequent itemsets
Data mining
Data streams
Incremental update
Single-pass mining
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Summary:Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than existing algorithm Moment for mining closed frequent itemsets over recent data streams.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2007.12.054