A Comparison of Outlier Detection Techniques for High-Dimensional Data

Abstract

Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly. Recently, a significant number of outlier detection methods have been witnessed and successfully applied in a wide range of fields, including medical health, credit card fraud and intrusion detection. They can be used for conventional data analysis. However, it is not a trivial work to identify rare behaviors or patterns out from complicated data. In this paper, we provide a brief overview of the outlier detection methods for high-dimensional data, and offer comprehensive understanding of the-state-of-the-art techniques of outlier detection for practitioners. Specifically, we firstly summarize the recent advances on outlier detection for high-dimensional data, and then make an extensive experimental comparison to the popular detection methods on public datasets. Finally, several challenging issues and future research directions are discussed.

Keywords

• data mining
• outlier detection
• high-dimensional data
• evaluation measurement