ó-SCLOPE : clustering categorical streams using attribute selection

Yap, Poh Hean and Ong, Kok-Leong 2005, ó-SCLOPE : clustering categorical streams using attribute selection, Lecture notes in computer science, vol. LNAI 3682, pp. 929-935.

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Title ó-SCLOPE : clustering categorical streams using attribute selection
Author(s) Yap, Poh Hean
Ong, Kok-Leong
Journal name Lecture notes in computer science
Volume number LNAI 3682
Start page 929
End page 935
Publisher Springer-Verlag
Place of publication Berlin, Germany
Publication date 2005-08-19
ISSN 0302-9743
1611-3349
Keyword(s) computer science
algorithms
data storage equipment
attribute selection
clustering accuracy
data acquisition
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 σ-SCLOPE, a novel algorithm based on SCLOPE’s intuitive observation about cluster histograms. Unlike SCLOPE however, our algorithm consumes less memory per window and has a better clustering runtime for the same data stream in a given window. This positions σ-SCLOPE as a more attractive option over SCLOPE if a minor lost of clustering accuracy is insignificant in the application.
Notes Book Title : Knowledge-Based Intelligent Information and Engineering Systems
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2005, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003315

Document type: Journal Article
Collection: School of Information Technology
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