Abstract
Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density (ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.
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Foundation item: Project(61362021) supported by the National Natural Science Foundation of China; Project(2016GXNSFAA380149) supported by Natural Science Foundation of Guangxi Province, China; Projects(2016YJCXB02, 2017YJCX34) supported by Innovation Project of GUET Graduate Education, China; Project(2011KF11) supported by the Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education, China
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Shou, Zy., Li, My. & Li, Sm. Outlier detection based on multi-dimensional clustering and local density. J. Cent. South Univ. 24, 1299–1306 (2017). https://doi.org/10.1007/s11771-017-3535-4
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DOI: https://doi.org/10.1007/s11771-017-3535-4