Outlier detection by example |
| |
Authors: | Email author" target="_blank">Cui?ZhuEmail author Hiroyuki?Kitagawa Spiros?Papadimitriou Christos?Faloutsos |
| |
Affiliation: | (1) College of Computer Science, Beijing University of Technology, Beijing, 100124, People’s Republic of China;(2) Graduate School of Systems and Information Engineering, Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan;(3) IBM T.J. Watson, Hawthorne, NY, USA;(4) Carnegie Mellon University, Pittsburgh, PA, USA |
| |
Abstract: | Outlier detection is a useful technique in such areas as fraud detection, financial analysis and health monitoring. Many recent
approaches detect outliers according to reasonable, pre-defined concepts of an outlier (e.g., distance-based, density-based,
etc.). However, the definition of an outlier differs between users or even datasets. This paper presents a solution to this
problem by including input from the users. Our OBE (Outlier By Example) system is the first that allows users to provide examples
of outliers in low-dimensional datasets. By incorporating a small number of such examples, OBE can successfully develop an
algorithm by which to identify further outliers based on their outlierness. Several algorithmic challenges and engineering
decisions must be addressed in building such a system. We describe the key design decisions and algorithms in this paper.
In order to interact with users having different degrees of domain knowledge, we develop two detection schemes: OBE-Fraction
and OBE-RF. Our experiments on both real and synthetic datasets demonstrate that OBE can discover values that a user would
consider outliers. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|