ERDOF:基于相对熵权密度离群因子的离群点检测算法

ERDOF:outlier detection algorithm based on entropy weight distance and relative density outlier factor

针对现有离群点检测算法在复杂数据分布和高维度数据集上精度低的问题,提出了一种基于相对熵权密度离群因子的离群点检测算法。首先引入熵权距离取代欧氏距离以提高离群点检测精度。然后结合自然邻居的概念对数据对象进行高斯核密度估计。同时提出相对距离来刻画数据对象偏离邻域的程度,提高所提算法在低密度区域检测离群点的能力。最后提出相对熵权密度离群因子来刻画数据对象的离群程度。在人工数据集和真实数据集下进行的实验表明,所提算法能有效适应各种数据分布和高维数据的离群点检测。

An outlier detection algorithm based on entropy weight distance and relative density outlier factor was proposed to solve the problem of low accuracy in complex data distribution and high dimensional data sets.Firstly,entropy weight distance was introduced instead of euclidean distance to improve the detection accuracy of outliers.Then,the Gaussian kernel density estimation was carried out for the data object based on the concept of natural neighbor.At the same time,relative distance was proposed to describe the degree of the data object deviating from the neighborhood and improve the ability of the algorithm to detect outliers in the low-density region.Finally,the entropy weight distance and relative density outlier factor were proposed to describe the degree of outliers.Experiments with artificial data sets and real data sets show that the proposed algorithm can effectively adapt to various data distributions and outlier detection of high-dimensional data.