LODES: Local Density Meets Spectral Outlier Detection

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2016 SIAM International Conference on Data Mining (SDM)LODES: Local Density Meets Spectral Outlier DetectionSaket Sathe and Charu AggarwalSaket Sathe and Charu Aggarwalpp.171 - 179Chapter DOI:https://doi.org/10.1137/1.9781611974348.20PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract The problem of outlier detection has been widely studied in existing literature because of its numerous applications in fraud detection, medical diagnostics, fault detection, and intrusion detection. A large category of outlier analysis algorithms have been proposed, such as proximity-based methods and local density-based methods. These methods are effective in finding outliers distributed along linear manifolds. Spectral methods, however, are particularly well suited to finding outliers when the data is distributed along manifolds of arbitrary shape. In practice, the underlying manifolds may have varying density, as a result of which a direct use of spectral methods may not be effective. In this paper, we show how to combine spectral techniques with local density-based methods in order to discover interesting outliers. We present experimental results demonstrating the effectiveness of our approach with respect to well-known competing methods. Previous chapter Next chapter RelatedDetails Published:2016eISBN:978-1-61197-434-8 https://doi.org/10.1137/1.9781611974348Book Series Name:ProceedingsBook Code:PRDT16Book Pages:1-867