Resource-aware kernel density estimators over streaming data

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Resource-aware kernel density estimators over streaming data
A fundamental building block of many data mining and analysis approaches is density estimation as it provides a comprehensive statistical model of a data distribution. For that reason, its application to transient data streams is highly desirable. A convenient, nonparametric method for density estimation utilizes kernels. However, its computational complexity collides with the rigid processing requirements of data streams. In this work, we present a new approach to this problem that combines linear processing cost with a constant amount of allocated memory. Our approach also supports a dynamic memory adaptation to changing system resources. Categories and Subject Descriptors G.3 [Probability and Statistics]: Nonparametric statistics General Terms Algorithms, Performance Keywords Data Streams, Kernel Density Estimation
Christoph Heinz, Bernhard Seeger
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where CIKM
Authors Christoph Heinz, Bernhard Seeger
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