An EM-based algorithm for clustering data streams in sliding windows

Dang, Xuan Hong, Lee, Vincent, Ng, Wee Keong, Ciptadi, Arridhana and Ong, Kok Leong 2009, An EM-based algorithm for clustering data streams in sliding windows, Lecture notes in computer science, vol. 5463, pp. 230-235, doi: 10.1007/978-3-642-00887-0_18.

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Title An EM-based algorithm for clustering data streams in sliding windows
Author(s) Dang, Xuan Hong
Lee, Vincent
Ng, Wee Keong
Ciptadi, Arridhana
Ong, Kok Leong
Journal name Lecture notes in computer science
Volume number 5463
Start page 230
End page 235
Total pages 6
Publisher Springer
Place of publication Heidelberg, Germany
Publication date 2009
ISSN 0302-9743
Summary Cluster analysis has played a key role in data understanding. When such an important data mining task is extended to the context of data streams, it becomes more challenging since the data arrive at a mining system in one-pass manner. The problem is even more difficult when the clustering task is considered in a sliding window model which requiring the elimination of outdated data must be dealt with properly. We propose SWEM algorithm that exploits the Expectation Maximization technique to address these challenges. SWEM is not only able to process the stream in an incremental manner, but also capable to adapt to changes happened in the underlying stream distribution.
Language eng
DOI 10.1007/978-3-642-00887-0_18
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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