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A local density-based outlier detection method for high dimension data | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 140، دوره 13، شماره 1، خرداد 2022، صفحه 1683-1699 اصل مقاله (1.39 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.5784 | ||
| نویسندگان | ||
| Shahad Adel Abdulghafoor* 1، 2؛ Lekaa Ali Mohamed1 | ||
| 1University of Baghdad, College of Management and Economics, Department Of Statistics, Iraq | ||
| 2Ministry of planning,Central Statistical Organization, Iraq | ||
| تاریخ دریافت: 04 خرداد 1400، تاریخ پذیرش: 27 مهر 1400 | ||
| چکیده | ||
| The researchers faced challenges in the outlier detection process, mainly when deals with the high dimensional dataset; to handle this problem, we use The principal component analysis. Outlier detection or anomaly detection, with local density-based methods, compares the density of observation with the surrounding local density neighbors. We apply the outlier score as a measure of comparison. In this research, we choose different density estimation functions and calculated different distances. Weighted kernel density estimation with adaptive bandwidth for multivariate kernel density estimation(Gaussian) considered the $KNN$ and RNN. $KNN$ is considered too for the Epanenchnikov kernel density estimation. Lastly, we estimate the LOF as a base method in detecting outliers. Extensive experiments on a synthetic dataset have shown that RKDOS and EPA are more efficient than LOF using the precision evaluation criterion. | ||
| کلیدواژه‌ها | ||
| local density؛ K-nearest neighbor؛ R-nearest neighbor؛ outlier score؛ WKDE | ||
| مراجع | ||
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