Chapter

Advances in Knowledge Discovery and Data Mining

Volume 2336 of the series Lecture Notes in Computer Science pp 535-548

Date:

Enhancing Effectiveness of Outlier Detections for Low Density Patterns

  • Jian TangAffiliated withDepartment of Computer Science and Engineering, Chinese University of Hong Kong
  • , Zhixiang ChenAffiliated withDepartment of Computer Science, University of Texas at Pan-America
  • , Ada Wai-chee FuAffiliated withDepartment of Computer Science and Engineering, Chinese University of Hong Kong
  • , David W. CheungAffiliated withDepartment of Computer Science and Information Systems, University of Hong Kong

Abstract

Outlier detection is concerned with discovering exceptional behaviors of objects in data sets. It is becoming a growingly useful tool in applications such as credit card fraud detection, discovering criminal behaviors in e-commerce, identifying computer intrusion, detecting health problems, etc. In this paper, we introduce a connectivity-based outlier factor (COF) scheme that improves the effectiveness of an existing local outlier factor (LOF) scheme when a pattern itself has similar neighbourhood density as an outlier. We give theoretical and empirical analysis to demonstrate the improvement in effectiveness and the capability of the COF scheme in comparison with the LOF scheme.