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- W1835592088 abstract "In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector (SPOT), to identify outliers embedded in subspaces. Sparse Subspace Template (SST), a set of subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. Multi-Objective Genetic Algorithm (MOGA) is employed as an effective search method for finding outlying subspaces from training data to construct SST. SST is able to carry out online self-evolution in the detection stage to cope with dynamics of data streams. The experimental results demonstrate the efficiency and effectiveness of SPOT in detecting outliers in high-dimensional data streams.KeywordsData StreamOutlier DetectionDetection StageMultiobjective Genetic AlgorithmKernel Density FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W1835592088 created "2016-06-24" @default.
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- W1835592088 date "2009-01-01" @default.
- W1835592088 modified "2023-10-16" @default.
- W1835592088 title "Detecting Projected Outliers in High-Dimensional Data Streams" @default.
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- W1835592088 doi "https://doi.org/10.1007/978-3-642-03573-9_53" @default.
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