Enhancing Effectiveness of Outlier Detections for Low Density Patterns

  • Jian Tang
  • Zhixiang Chen
  • Ada Wai-chee Fu
  • David W. Cheung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2336)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jian Tang
    • 1
  • Zhixiang Chen
    • 2
  • Ada Wai-chee Fu
    • 1
  • David W. Cheung
    • 3
  1. 1.Department of Computer Science and EngineeringChinese University of Hong KongShatinHong Kong
  2. 2.Department of Computer ScienceUniversity of Texas at Pan-AmericaTexasUSA
  3. 3.Department of Computer Science and Information SystemsUniversity of Hong KongPokfulamHong Kong
  4. 4.Memorial University of NewfoundlandCanada

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