Title: Outlier detection based on cluster outlier factor and mutual density

Authors: Zhongping Zhang; Mengfan Zhu; Jingyang Qiu; Cong Liu; Debin Zhang; Jie Qi

Addresses: School of Information Science and Engineering, Yanshan University, Qinghuangdao, Hebei, 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, 438 West Section of Hebei Street, Haigang District, Qinhuangdao City, Hebei Province, 066004, China ' School of Information Science and Engineering, Yanshan University, Qinghuangdao, Hebei, 066004, China ' School of Information Science and Engineering, Yanshan University, Qinghuangdao, Hebei, 066004, China ' School of Information Science and Engineering, Yanshan University, Qinghuangdao, Hebei, 066004, China ' Hebei Education Examinations Authority, 231 Hongqi Street, Shijiazhuang City, 050000, Hebei Province, China ' The First Middle School of Qian An Country, Qianan, Jilin, 131400, 347 Wutong Road, China

Abstract: Outlier detection is an important task in data mining with numerous applications. Recent years, the study on outlier detection is very active, many algorithms were proposed including based on clustering. However, most outlier detection algorithms based on clustering often need parameters and it is very difficult to select a suitable parameter for different data set. In order to solve this problem, an outlier detection algorithm called outlier detection based on cluster outlier factor and mutual density is proposed in this paper. The mutual density and γ density is used to construct decision graph. The data points with γ density anomalously large in decision graph are treated as cluster centres. This algorithm detect the boundary of outlier cluster using cluster outlier factor called cluster outlier factor (COF), it can automatic find the parameter. This method can achieve good performance in clustering and outlier detection which be shown in the experiments.

Keywords: data mining; outlier; mutual density; γ density; cluster outlier factor; database; financial fraud detection; information security; medical and public health detection; outlier detection.

DOI: 10.1504/IJIIDS.2019.102329

International Journal of Intelligent Information and Database Systems, 2019 Vol.12 No.1/2, pp.91 - 108

Received: 21 Sep 2018
Accepted: 27 Feb 2019
Published online: 18 Sep 2019 *

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