An efficient outlier detection method based on multi-factor fusion
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NETL
NSTL
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基于邻域粗糙集的对象邻域相对比和对象重要度等粒化特征,提出了改进的基于邻域粗糙熵的多因素融合的离群点检测(neighborhood rough entropy-based outlier,NREOD)算法.在加利福尼亚大学尔湾分校(University of CaliforniaIrvine,UCI)数据库的标准数据集上的对比实验表明,NREOD算法在不同类型的数据集的离群检测的误判率更低,并且有更好的适应性和有效性.此算法为混合型属性数据集的离群检测研究与应用提供了一条新的有效途径.
Based on granularity characteristics of relative ratio of object neighborhood and importance of objects in neighborhood rough sets,an improved outlier detection method(neighborhood rough entropy-based outlier,NREOD)based on neighborhood rough entropy and multi-factor fusion is proposed.Comparison experiments on standard data sets in University of CaliforniaIrvine(UCI)databases show that the NREOD algorithm has a lower false positive rate for outlier detection in different types of data sets,and has better adaptability and effectiveness.This algorithm provides a new effective way for the research and application of outlier detection in mixed attribute data sets.
data miningoutlier detectionneighborhood rough setneighborhood rough entropymulti-factor fusion
NETL
NSTL
万方数据
