高维空间中孤立点检测的统一子空间孤立点包围框架
A Unified Subspace Outlier Ensemble Framework for Outlier Detection in High Dimensional Spaces
摘要：
离群点检测的任务是发现与其余大量数据相比异常的小数据对象组。这种异常值的检测对于许多应用（例如欺诈检测和客户迁移）是重要的。大多数这样的应用程序是高维域，其中数据可能包含数百个维度。然而，孤立点检测问题本身并没有很好的定义，现有的定义都没有被广泛接受，特别是在高维空间。在本文中，我们的第一个贡献是提出了一个统一的框架，离群检测高维空间从集成学习的观点。在我们的新框架中，每个数据对象的离群性通过使用组合函数融合不同子空间中的离群因子来测量。因此，我们表明，所有现有的离群点检测的研究可以被视为特殊情况下，在统一的框架，考虑的子空间集和使用的组合函数的类型。此外，为了证明基于集成学习的离群值检测框架的有用性，我们开发了一个非常简单和快速的算法，即SOE 1（使用一维子空间的子空间离群值Encrimination），其中只有一维子空间用于从大型分类数据集中挖掘离群值。SOE 1算法只需要对数据集进行两次扫描，因此在实际的数据采掘应用中非常有吸引力。在真实数据集和大型合成数据集上的实验结果表明：（1）SOE 1在识别真实离群点方面与现有离群点检测算法具有相当的性能;（2）SOE 1可以比迄今为止已知的最快离群点检测算法之一快一个数量级。
英文摘要：
The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Detection of such outliers is important for many applications such as fraud detection and customer migration. Most such applications are high dimensional domains in which the data may contain hundreds of dimensions. However, the outlier detection problem itself is not well defined and none of the existing definitions are widely accepted, especially in high dimensional space. In this paper, our first contribution is to propose a unified framework for outlier detection in high dimensional spaces from an ensemble-learning viewpoint. In our new framework, the outlying-ness of each data object is measured by fusing outlier factors in different subspaces using a combination function. Accordingly, we show that all existing researches on outlier detection can be regarded as special cases in the unified framework with respect to the set of subspaces considered and the type of combination function used. In addition, to demonstrate the usefulness of the ensemble-learning based outlier detection framework, we developed a very simple and fast algorithm, namely SOE1 (Subspace Outlier Ensemble using 1-dimensional Subspaces) in which only subspaces with one dimension is used for mining outliers from large categorical datasets. The SOE1 algorithm needs only two scans over the dataset and hence is very appealing in real data mining applications. Experimental results on real datasets and large synthetic datasets show that: (1) SOE1 has comparable performance with respect to those state-of-art outlier detection algorithms on identifying true outliers and (2) SOE1 can be an order of magnitude faster than one of the fastest outlier detection algorithms known so far.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
