Journal
DATA & KNOWLEDGE ENGINEERING
Volume 68, Issue 1, Pages 1-27Publisher
ELSEVIER
DOI: 10.1016/j.datak.2008.08.006
Keywords
Data mining; Incremental graph-based clustering; Stream data clustering; Recurrent change; Knowledge acquisition
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We present an incremental graph-based clustering algorithm whose design was motivated by a need to extract and retain meaningful information from data streams produced by applications such as large scale surveillance, network packet inspection and financial transaction monitoring. To this end, the method we propose utilises representative points to both incrementally cluster new data and to selectively retain important cluster information within a knowledge repository. The repository can then be subsequently used to assist in the processing of new data, the archival of critical features for off-line analysis, and in the identification of recurrent patterns. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.
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