Online mining of frequent sets in data streams with error guarantee

Dang, Xuan Hong, Ng, Wee-Keong and Ong, Kok-Leong 2008, Online mining of frequent sets in data streams with error guarantee, Knowledge and information systems, vol. 16, no. 2, pp. 245-258.

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Title Online mining of frequent sets in data streams with error guarantee
Author(s) Dang, Xuan Hong
Ng, Wee-Keong
Ong, Kok-Leong
Journal name Knowledge and information systems
Volume number 16
Issue number 2
Start page 245
End page 258
Total pages 14
Publisher Springer UK
Place of publication London, England
Publication date 2008-08
ISSN 0219-1377
0219-3116
Keyword(s) data mining
frequent set mining
data stream
online algorithm
error guarantee
Summary For most data stream applications, the volume of data is too huge to be stored in permanent devices or to be thoroughly scanned more than once. It is hence recognized that approximate answers are usually sufficient, where a good approximation obtained in a timely manner is often better than the exact answer that is delayed beyond the window of opportunity. Unfortunately, this is not the case for mining frequent patterns over data streams where algorithms capable of online processing data streams do not conform strictly to a precise error guarantee. Since the quality of approximate answers is as important as their timely delivery, it is necessary to design algorithms to meet both criteria at the same time. In this paper, we propose an algorithm that allows online processing of streaming data and yet guaranteeing the support error of frequent patterns strictly within a user-specified threshold. Our theoretical and experimental studies show that our algorithm is an effective and reliable method for finding frequent sets in data stream environments when both constraints need to be satisfied.
Notes Published online September 22, 2007
Language eng
Field of Research 080604 Database Management
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, Springer-Verlag London Limited
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017297

Document type: Journal Article
Collection: School of Engineering and Information Technology
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