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
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||