Outlier Detection Based on Cluster Outlier Factor and Mutual Density

  • Zhongping ZhangEmail author
  • Mengfan ZhuEmail author
  • Jingyang Qiu
  • Cong Liu
  • Debin Zhang
  • Jie Qi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 986)


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 that 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 which combining the natural neighbor search algorithm of the Natural Outlier Factor (NOF) algorithm and based on the Density and Distance Cluster (DDC) algorithm. 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 centers. 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.


Data mining Outlier Mutual density γ density Cluster outlier factor 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
  2. 2.The Key Laboratory for Computer Virtual Technology and System Integration of Hebei ProvinceQinhuangdaoChina
  3. 3.Hebei Education Examinations AuthorityShijiazhuangChina
  4. 4.The First Middle School of Qian An CountryQian’anChina

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