Unsupervised Clustering In Streaming Data

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 clu...

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Bibliographic Details
Main Authors: Tasoulis, D.K., Adams, N.M., Hand, D.J.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Clustering methods
Data acquisition
Data mining
Data models
Educational institutions
Kernel
Partitioning algorithms
Scalability
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Summary: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
ISSN:2375-9232
2375-9259
DOI:10.1109/ICDMW.2006.165