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- W2078666961 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." @default.
- W2078666961 created "2016-06-24" @default.
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- W2078666961 date "2006-01-01" @default.
- W2078666961 modified "2023-09-26" @default.
- W2078666961 title "Resource-aware kernel density estimators over streaming data" @default.
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- W2078666961 doi "https://doi.org/10.1145/1183614.1183772" @default.
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