EStream: online mining of frequent sets with precise error guarantee

Dang, Xuan Hong, Ng, Wee-Keong and Ong, Kok-Leong 2006, EStream: online mining of frequent sets with precise error guarantee, Lecture notes in computer science, vol. 4081, pp. 312-321, doi: 10.1007/11823728_30.

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Title EStream: online mining of frequent sets with precise error guarantee
Author(s) Dang, Xuan Hong
Ng, Wee-Keong
Ong, Kok-Leong
Journal name Lecture notes in computer science
Volume number 4081
Start page 312
End page 321
Publisher Spinger-Verlag
Place of publication Berlin, Germany
Publication date 2006
ISSN 0302-9743
Summary In data stream applications, a good approximation obtained in a timely  manner is often better than the exact answer that’s delayed beyond the window of opportunity. Of course, the quality of the approximate is as important as its timely delivery. Unfortunately, algorithms capable of online processing do not conform strictly to a precise error guarantee. Since online processing is essential and so is the precision of the error, it is necessary that stream algorithms meet both criteria. Yet, this is not the case for mining frequent sets in data streams. We present EStream, a novel algorithm that allows online processing while producing results strictly within the error bound. Our theoretical and experimental results show that EStream is a better candidate for finding frequent sets in data streams, when both constraints need to be satisfied.
Notes Book Title: Data Warehousing and Knowledge Discovery
Language eng
DOI 10.1007/11823728_30
Field of Research 080605 Decision Support and Group Support Systems
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2006, Springer-Verlag Berlin Heidelberg
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