Incremental and adaptive clustering stream data over sliding window

Dang, Xuan Hong, Lee, Vincent C. S., Ng, Wee Keong and Ong, Kok Leong 2009, Incremental and adaptive clustering stream data over sliding window, Lecture notes in computer science, vol. 5690, pp. 660-674, doi: 10.1007/978-3-642-03573-9_55.

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Title Incremental and adaptive clustering stream data over sliding window
Author(s) Dang, Xuan Hong
Lee, Vincent C. S.
Ng, Wee Keong
Ong, Kok Leong
Journal name Lecture notes in computer science
Volume number 5690
Start page 660
End page 674
Total pages 15
Publisher Springer
Place of publication Heidelberg, Germany
Publication date 2009
ISSN 0302-9743
Summary Cluster analysis has played a key role in data stream understanding. The problem is difficult when the clustering task is considered in a sliding window model in which the requirement of outdated data elimination must be dealt with properly. We propose SWEM algorithm that is designed based on the Expectation Maximization technique to address these challenges. Equipped in SWEM is the capability to compute clusters incrementally using a small number of statistics summarized over the stream and the capability to adapt to the stream distribution’s changes. The feasibility of SWEM has been verified via a number of experiments and we show that it is superior than Clustream algorithm, for both synthetic and real datasets.
Language eng
DOI 10.1007/978-3-642-03573-9_55
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2009, Springer-Verlag
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