Capabilities of outlier detection schemes in large datasets, framework and methodologies

Abstract. Outlier detection is concerned with discovering exceptional behaviors of objects. Its theoretical principle and practical implementation lay a foundation for some important applications such as credit card fraud detection, discovering criminal behaviors in e-commerce, discovering computer intrusion, etc. In this paper, we first present a unified model for several existing outlier detection schemes, and propose a compatibility theory, which establishes a framework for describing the capabilities for various outlier formulation schemes in terms of matching users’ intuitions. Under this framework we show that the density-based scheme is more powerful than the distance-based scheme when a dataset contains patterns with diverse characteristics. The densitybased scheme, however, is less effective when the patterns are of comparable densities with the outliers. We then introduce a connectivity-based scheme that improves the effectiveness of the density-based scheme when a patt...

• Type: Journal
• Year: 2007
• Where: KAIS
• Authors: Jian Tang, Zhixiang Chen, Ada Wai-Chee Fu, David W. Cheung
• Keywords: KAIS 2007, Outlier, Outlier Detection, Outlier Detection Schemes