Unsupervised Clustering In Streaming Data

Authors:
Dimitris K. Tasoulis;Niall M. Adams;David J. Hand
Affiliations:
Imperial College London, South Kensington Campus;Imperial College London, South Kensington Campus;Imperial College London, South Kensington Campus
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Abstract

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.