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- W3156256341 abstract "Detecting outlier is a challenging task. At present, all methods calculate the outlier degree based on euclidean distance, but with the increase of size, the distance characteristics become weaker, the detection accuracy is low, and the K value is sensitive. In this paper, we present an outlier detection method based on the symmetry and curvature threshold idea named SYMMETRY. On the basis of KNN (K-NearestNeighbor), with the help of LNN (Locally Nearest Neighbor), symmetry is introduced, the k-value calculation formula is proposed, and the threshold is obtained automatically by curvature method. The main advantages of our method are as follows: based on symmetry, the outlier characteristics are more obvious, but the cluster points are not affected. Compared with distance-based methods, the defects and parameter sensitivity of distance characteristics are weakened. The algorithm does not require parameter input with strong stability and high precision. For the same amount of data, the larger the dimension, the faster the program runs. In the experimental part, the SYMMETRY algorithm will be compared with DB, LOF, RBDA, LDOF, ODIN, RDOS, INFLO, these seven classical KNN-based algorithms, using ROC curve evaluation algorithm, and finally prove that our algorithm performs well." @default.
- W3156256341 created "2021-04-26" @default.
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- W3156256341 date "2020-12-25" @default.
- W3156256341 modified "2023-09-27" @default.
- W3156256341 title "An Outlier Detection Method Based On Symmetry and Curvature Threshold" @default.
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- W3156256341 doi "https://doi.org/10.1145/3447450.3447457" @default.
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