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A relevant subspace based contextual outlier mining algorithm

机译：基于相关子空间的上下文离群值挖掘算法

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摘要

For high-dimensional and massive data sets, a relevant subspace based contextual outlier detection algorithm is proposed. Firstly, the relevant subspace, which can effectively describe the local distribution of the various data sets, is redefined by using local sparseness of attribute dimensions. Secondly, a local outlier factor calculation formula in the relevant subspace is defined with probability density of local data sets, and the formula can effectively reflect the outlier degree of data object that does not obey the distribution of the local data set in the relevant subspace. Thirdly, attribute dimensions of constituting the relevant subspace and local outlier factor are defined as the contextual information, which can improve the interpretability and comprehensibility of outlier. Fourthly, the selection of N data objects with the greatest local outlier factor value is defined as contextual outliers. In the end, experimental results validate the effectiveness of the algorithm by using UCI data sets. (C) 2016 Elsevier B.V. All rights reserved.
机译：针对高维海量数据集，提出了一种基于子空间的上下文离群值检测算法。首先，通过使用属性维的局部稀疏性来重新定义可以有效描述各种数据集的局部分布的相关子空间。其次，用局部数据集的概率密度定义了相关子空间中的局部离群因子计算公式，该公式可以有效地反映不服从局部子集中相关数据集分布的数据对象的离群度。第三，将构成相关子空间的属性维和局部离群因素定义为上下文信息，可以提高离群值的可解释性和可理解性。第四，将具有最大局部离群因子值的N个数据对象的选择定义为上下文离群。最后，实验结果通过使用UCI数据集验证了算法的有效性。 （C）2016 Elsevier B.V.保留所有权利。

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