Outlier detection based on cluster outlier factor and mutual density. (9th September 2019)

• Record Type: Journal Article
• Authors: Zhang, Zhongping; Zhu, Mengfan; Qiu, Jingyang; Liu, Cong; Zhang, Debin; Qi, Jie
• Is Part Of: International journal of intelligent information and database systems. Volume 12:Number 1/2(2019)
• Page Start: 91
• Page End: 108
• Publication Date: 2019-09-09
• Subjects: data mining -- outlier -- mutual density -- γ density -- cluster outlier factor -- database -- financial fraud detection -- information security -- medical and public health detection -- outlier detection; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

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.