Distance-Based Outlier Detection: Consolidation and Renewed Bearing

Detecting outliers in data is an important problem with interesting applications in a myriad of domains ranging from data cleaning to financial fraud detection and from network intrusion detection to clinical diagnosis of diseases. Over the last decade of research, distance-based outlier detection algorithms have emerged as a viable, scalable, parameter-free alternative to the more traditional statistical approaches. In this paper we assess several distance-based outlier detection approaches and evaluate them. We begin by surveying and examining the design landscape of extant approaches, while identifying key design decisions of such approaches. We then implement an outlier detection framework and conduct a factorial design experiment to understand the pros and cons of various optimizations proposed by us as well as those proposed in the literature, both independently and in conjunction with one another, on a diverse set of real-life datasets. To the best of our knowledge this is the...

• Type: Journal
• Year: 2010
• Where: PVLDB
• Authors: Gustavo Henrique Orair, Carlos Teixeira, Ye Wang, Wagner Meira Jr., Srinivasan Parthasarathy
• Keywords: Distance-based Outlier Detection, Outlier, Outlier Detection Algorithms, PVLDB 2010