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ó-SCLOPE : clustering categorical streams using attribute selection

journal contribution
posted on 2005-08-19, 00:00 authored by Poh Hean Yap, Kok-Leong Ong
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.

History

Journal

Lecture notes in computer science

Volume

LNAI 3682

Pagination

929 - 935

Publisher

Springer-Verlag

Location

Berlin, Germany

ISSN

0302-9743

eISSN

1611-3349

Language

eng

Notes

Book Title : Knowledge-Based Intelligent Information and Engineering Systems

Publication classification

C1 Refereed article in a scholarly journal; C Journal article

Copyright notice

2005, Springer-Verlag Berlin Heidelberg

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