An outlier detection algorithm based on an integrated outlier factor

Article type: Research Article

Authors: Zhou, Hongfang^{a; b; *} | Liu, Hongjiang^{a; b} | Zhang, Yingjie^{a; b} | Zhang, Yao^{a; b}

Affiliations: [a] School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, Shaanxi 710048, China | [b] Shaanxi Key Laboratory of Network Computing and Security Technology, Xi’an, Shaanxi 710048, China

Correspondence: [*] Corresponding author: Hongfang Zhou, School of Computer Science and Engineering, Xi’an University of Technology, NO. 5 South Jinhua Road, Xi’an, Shaanxi 710048, China. Tel.: +86 18966606688; E-mail: zhouhf@xaut.edu.cn.

Abstract: Outlier detection is an important task in data mining. In this paper, a novel outlier detection algorithm is proposed, which integrates the local density with the global distance seamlessly. In the proposed method, an integrated outlier factor is used to measure the detecting accuracy. A comprehensive experimental study on both synthetic and real-life datasets shows that the proposed method is more effective than some typical outlier detection methods, including Relative Density-based Outlier Score (RDOS), INFLuenced Outlierness (INFLO), Local Outlier Factor (LOF) and Local Distance-based Outlier detection Factor (LDOF).

Keywords: Outlier detection, local density, global distance, double parameter

DOI: 10.3233/IDA-184227

Journal: Intelligent Data Analysis, vol. 23, no. 5, pp. 975-990, 2019