A robust approach for outlier detection based on the ratio of number of reverse neighbors to neighbors

• Published in: Pattern analysis and applications : PAA Vol. 28; no. 1
• Main Authors: Heydari-Gharaei, Reza, Sharifi, Rasoul, Kashef, Shima, Nezamabadi-pour, Hossein
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2025; Springer Nature B.V
• Subjects: Adaptive algorithms; Computer Science; Data analysis; Data mining; Density; Methods; Outliers (statistics); Parameter robustness; Parameter sensitivity; Pattern Recognition; Robustness; Theoretical Advances; Outlier detection; Reverse nearest neighbor; Nearest neighbors; Density; Anomaly detection; Distance

Outlier detection is an important issue in data mining, which has a wide range of applications in medicine, economics, video search, and credit card fraud detection. Many outlier detection methods have recently been developed. Most of the existing methods act based on the distance or density. Since...