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- W2912785012 abstract "Outlier detection is an important sub-field of data mining and studied intensively by researchers in the past decades. For neighborhood-based outlier detection methods like KNN and LOF, different settings in the number of neighbors (indicated by a parameter <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$k$ </tex-math></inline-formula> ) would greatly affect the model’s performance. Thereby, there are some recent studies which focus on identifying the optimal value of <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$k$ </tex-math></inline-formula> by analyzing the global or local structure of the dataset. But, we argue that neighborhood-based outlier detection model could obtain an improvement in performance without parameter tuning. In this paper, from a novel angle of view, we adopt a uniform sampling strategy to generate a series of local proximity graphs and propose a new adaptive outlier detection model named anomaly pattern score which does not rely on the <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$k$ </tex-math></inline-formula> tuning. In addition, the theoretical analysis of the effectiveness of the proposed model is conducted as well. The extensive experiments on both synthetic and real-world datasets show that the proposed model outperforms the state-of-the-art algorithms on most datasets." @default.
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- W2912785012 date "2019-01-01" @default.
- W2912785012 modified "2023-10-15" @default.
- W2912785012 title "Applying Anomaly Pattern Score for Outlier Detection" @default.
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- W2912785012 doi "https://doi.org/10.1109/access.2019.2895094" @default.
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