关键词: 离群点, 数据挖掘, 聚类离群因子, 相互密度, &gamma, 密度

Abstract: Most outlier detection algorithms based on clustering often need to input parameters artificially,which was difficult to select a suitable parameter for different datasets.To solve this problem,an outlier detection algorithm based on cluster outlier factor and mutual density was proposed by combining the natural neighbor search algorithm of NOF algorithm with DPC algorithm.The mutual density and γ density was used to construct decision graph,and the data points with gamma-density anomalously large in decision graph were treated as cluster centers.According to the Cluster Outlier Factor(COF),the boundary of outlier cluster was detected to find the parameter automatically.The experiments showed that the proposed method could achieve good performance in clustering and outlier detection.

Key words: outlier, data mining, cluster outlier factor, mutual density, &gamma, density