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

• Type: Conference
• Year: 2006
• Where: CIKM
• Authors: Christoph Heinz, Bernhard Seeger
• Keywords: CIKM 2006, Data Streams, Density Estimation, Estimation Utilizes Kernels, Information Management