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
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-024-01372-y

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 each of these methods has its inherent disadvantage, we proposed a method which has the advantages of both distance-based and density-based methods. The proposed method is inspired by the basic idea that outliers are usually more distant neighbors to their nearest neighbors. The proposed method consists of three different parts. Each of these parts considers the distance, density, or location of objects, and finally we reach an optimal and efficient algorithm by combining these parts. Our algorithm is based on k nearest neighbor; in addition, we also use another kind of adaptive and extended neighborhood in order to provide more accurate results. Furthermore, the proposed method is robust and has little sensitivity to changes in parameter k . Numerical experiments and comparing with well-known algorithms are performed on both synthetic and real datasets in order to prove the efficiency and robustness of the proposed method.