Comparison of local outlier detection techniques in spatial multivariate data

详细信息    查看全文

• 作者：Marie Ernst ; Gentiane Haesbroeck
• 关键词：Local outliers ; Regularized minimum covariance determinant estimator ; Spatial data
• 刊名：Data Mining and Knowledge Discovery
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：31
• 期：2
• 页码：371-399
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Data Mining and Knowledge Discovery; Artificial Intelligence (incl. Robotics); Information Storage and Retrieval; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences;
• 出版者：Springer US
• ISSN：1573-756X
• 卷排序：31

文摘

Outlier detection techniques in spatial data should allow to identify two types of outliers: global and local ones. Local outliers typically have non-spatial attributes that strongly differ from those observed on their neighbors. Detecting local outliers requires to be able to work locally, on neighborhoods, in order to take into account the spatial dependence between the statistical units under consideration, even though the outlyingness is usually measured on the non-spatial variables. Many procedures have been outlined in the literature, but their number reduces when one wants to deal with multivariate non-spatial attributes. In this paper, focus is on the multivariate context. A review of existing procedures is done. A new approach, based on a two-step improvement of an existing one, is also designed and compared with the benchmarked methods by means of examples and simulations.