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ICDE
2009
IEEE

Tracking High Quality Clusters over Uncertain Data Streams

9 years 11 months ago
Tracking High Quality Clusters over Uncertain Data Streams
Recently, data mining over uncertain data streams has attracted a lot of attentions because of the widely existed imprecise data generated from a variety of streaming applications. In this paper, we try to resolve the problem of clustering over uncertain data streams. Facing uncertain tuples with different probability distributions, the clustering algorithm should not only consider the tuple value but also emphasis on its uncertainty. To fulfill these dual purposes, a metric named tuple uncertainty will be integrated into the overall procedure of clustering. Firstly, we survey uncertain data model and propose our uncertainty measurement and corresponding properties. Secondly, based on such uncertainty quantification method, we provide a two phase stream clustering algorithm and elaborate implementation detail. Finally, performance experiments over a number of real and synthetic data sets demonstrate the effectiveness and efficiency of our method.
Chen Zhang, Ming Gao, Aoying Zhou
Added 20 Oct 2009
Updated 20 Oct 2009
Type Conference
Year 2009
Where ICDE
Authors Chen Zhang, Ming Gao, Aoying Zhou
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