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• W2034758081 abstract "The Probability Density Function (PDF) is the fundamental data model for a variety of stream mining algorithms. Existing works apply the standard nonparametric Kernel Density Estimator (KDE) to approximate the PDF of data streams. As a result, the stream-based KDEs cannot accurately capture complex local density features. In this article, we propose the use of Local Region (LRs) to model local density information in univariate data streams. In-depth theoretical analyses are presented to justify the effectiveness of the LR-based KDE. Based on the analyses, we develop the General Local rEgion AlgorithM (GLEAM) to enhance the estimation quality of structurally complex univariate distributions for existing stream-based KDEs. A set of algorithmic optimizations is designed to improve the query throughput of GLEAM and to achieve its linear order computation. Additionally, a comprehensive suite of experiments was conducted to test the effectiveness and efficiency of GLEAM." @default.
• W2034758081 created "2016-06-24" @default.
• W2034758081 creator A5006756331 @default.
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• W2034758081 date "2014-08-25" @default.
• W2034758081 modified "2023-09-25" @default.
• W2034758081 title "A Framework for Exploiting Local Information to Enhance Density Estimation of Data Streams" @default.
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• W2034758081 doi "https://doi.org/10.1145/2629618" @default.
• W2034758081 hasPublicationYear "2014" @default.
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