loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)
Unsupervised Clustering In Streaming Data
Hong Kong, China
December 18-December 22
ISBN: 0-7695-2702-7
Dimitris K. Tasoulis, Imperial College London, South Kensington Campus
Niall M. Adams, Imperial College London, South Kensington Campus
David J. Hand, Imperial College London, South Kensington Campus
Tools for automatically clustering streaming data are becoming increasingly important as data acquisition technology continues to advance. In this paper we present an extension of conventional kernel density clustering to a spatio-temporal setting, and also develop a novel algorithmic scheme for clustering data streams. Experimental results demonstrate both the high efficiency and other benefits of this new approach.
Citation:
Dimitris K. Tasoulis, Niall M. Adams, David J. Hand, "Unsupervised Clustering In Streaming Data," icdmw, pp.638-642, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006
Usage of this product signifies your acceptance of the Terms of Use.