Local Outlier Detection Algorithm Based on Gaussian Kernel Density Function

  • Zhongping ZhangEmail author
  • Jiaojiao Liu
  • Chuangye Miao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 986)


With the rapid development of information technology, the structure of data resources is becoming more and more complex, and outlier mining is attracting more and more attention. Based on Gaussian kernel function, this paper considers three neighbors: k nearest neighbors, reverse k neighbors and shared nearest neighbors. A local outlier detection algorithm based on Gaussian kernel function is proposed. Firstly, the algorithm stores the nearest neighbors of each data object through kNN maps, including k-nearest neighbors, reverse k-nearest neighbors, and shared nearest neighbors, forming a kernel neighbor set S. Secondly, Estimating density of data objects through kernel density estimation KDE method. Finally, the relative density outlier factor RDOF is used to estimate the degree of data objects deviating from the neighborhood, and then determines whether the data objects are outliers, and the validity of the algorithm is proved on the real and synthetic data sets.


Data mining Outliers Gaussian kernel function Kernel density Kernel neighbor 


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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

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