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- W2053977334 abstract "Partition outlier using neighborhood radius has proven to be an effective distance-based detection algorithm. However, it is not yet clear how to choose the neighborhood radius d <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>min</sub> , and getting the value by trial and error is still been widely adopted. This paper presents a method to get the neighborhood radius from fractal dimensions which is used to describe the self-similarity of a dataset. We first discuss how to calculate the fractal dimensions and how to value d <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>min</sub> , and then we use this value in distance-based outlier detection algorithms. Finally, we verify the validity of this neighborhood radius calculation method by experimental results." @default.
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- W2053977334 date "2014-08-01" @default.
- W2053977334 modified "2023-10-16" @default.
- W2053977334 title "Outlier detection using neighborhood radius based on fractal dimension" @default.
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- W2053977334 doi "https://doi.org/10.1109/iccse.2014.6926468" @default.
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