Resource-aware kernel density estimators over streaming data

Authors:
Christoph Heinz;Bernhard Seeger
Affiliations:
University of Marburg, Germany;University of Marburg, Germany
Venue:
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Year:
2006

Citing 3
Cited 0

M-Kernel Merging: Towards Density Estimation over Data Streams

DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Mining data streams: a review

ACM SIGMOD Record
Exploring Data Streams with Nonparametric Estimators

SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management

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Abstract

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.