Multivariate Voronoi Outlier Detection for Time Series.
Multivariate Voronoi Outlier Detection for Time Series.
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时间序列的多元 Voronoi 异常值检测。
DOI：
10.1109/hic.2014.7038934
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Wang,MichelleYongmei
中科院分区：
文献类型：
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作者：
Zwilling,ChrisE;Wang,MichelleYongmei
孤立点检测是许多数据挖掘和分析应用程序的主要步骤，包括医疗保健和医学研究。本文提出了一种基于Voronoi图的多变量时间序列孤立点识别的通用方法，称为多变量Voronoi孤立点检测(MVOD)。该方法通过从参数或非参数形式的数据中设计和提取有效的属性或特征来应对多变量框架中的离群值。Voronoi图允许自动配置数据点的邻域关系，这有助于区分孤立点和非孤立点。实验结果表明，MVOD是一种准确、灵敏、稳健的多变量时间序列数据孤立点检测方法。
Outlier detection is a primary step in many data mining and analysis applications, including healthcare and medical research. This paper presents a general method to identify outliers in multivariate time series based on a Voronoi diagram, which we call Multivariate Voronoi Outlier Detection (MVOD). The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms. Voronoi diagrams allow for automatic configuration of the neighborhood relationship of the data points, which facilitates the differentiation of outliers and non-outliers. Experimental evaluation demonstrates that our MVOD is an accurate, sensitive, and robust method for detecting outliers in multivariate time series data.