SCLOPE: an algorithm for clustering data streams of categorical attributes

Ong, Kok-Leong, Li, Wenyuan, Ng, Wee-Keong and Lim, Ee-Peng 2004, SCLOPE: an algorithm for clustering data streams of categorical attributes, Lecture notes in computer science, vol. 3181/2004, pp. 209-218.

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Title SCLOPE: an algorithm for clustering data streams of categorical attributes
Author(s) Ong, Kok-Leong
Li, Wenyuan
Ng, Wee-Keong
Lim, Ee-Peng
Journal name Lecture notes in computer science
Volume number 3181/2004
Start page 209
End page 218
Publisher Springer-Verlag
Place of publication Berlin, Germany
Publication date 2004
ISSN 0302-9743
Summary Clustering is a difficult problem especially when we consider the task in the context of a data stream of categorical attributes. In this paper, we propose SCLOPE, a novel algorithm based on CLOPErsquos intuitive observation about cluster histograms. Unlike CLOPE however, our algo- rithm is very fast and operates within the constraints of a data stream environment. In particular, we designed SCLOPE according to the recent CluStream framework. Our evaluation of SCLOPE shows very promising results. It consistently outperforms CLOPE in speed and scalability tests on our data sets while maintaining high cluster purity; it also supports cluster analysis that other algorithms in its class do not.
Language eng
Field of Research 080604 Database Management
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2004, Springer-Verlag Berlin Heidelberg
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