A High-Dimensional Outlier Detection Approach Based on Local Coulomb Force

Traditional outlier detections are inadequate for high-dimensional data analysis due to the interference of distance tending to be concentrated ("curse of dimensionality"). Inspired by the Coulomb's law, we propose a new high-dimensional data similarity measure vector, which consists...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering 2023-06, Vol.35 (6), p.5506-5520
Hauptverfasser:	Zhu, Pengyun, Zhang, Chaowei, Li, Xiaofeng, Zhang, Jifu, Qin, Xiao
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Anomaly detection Data analysis Data centers Deviation Dimensional analysis Euclidean distance Force Force measurement High-dimensional outlier detection Indexes Interference local outlier coulomb force neighborhood outlier factor outlier coulomb resultant force Outliers (statistics) Similarity similarity metric Synthetic data Task analysis
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