Data Mining and Knowledge Discovery

, Volume 8, Issue 3, pp 275–300

On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms

  • Kenji Yamanishi
  • Jun-ichi Takeuchi
  • Graham Williams
  • Peter Milne
Article

DOI: 10.1023/B:DAMI.0000023676.72185.7c

Cite this article as:
Yamanishi, K., Takeuchi, J., Williams, G. et al. Data Mining and Knowledge Discovery (2004) 8: 275. doi:10.1023/B:DAMI.0000023676.72185.7c

Abstract

Outlier detection is a fundamental issue in data mining, specifically in fraud detection, network intrusion detection, network monitoring, etc. SmartSifter is an outlier detection engine addressing this problem from the viewpoint of statistical learning theory. This paper provides a theoretical basis for SmartSifter and empirically demonstrates its effectiveness. SmartSifter detects outliers in an on-line process through the on-line unsupervised learning of a probabilistic model (using a finite mixture model) of the information source. Each time a datum is input SmartSifter employs an on-line discounting learning algorithm to learn the probabilistic model. A score is given to the datum based on the learned model with a high score indicating a high possibility of being a statistical outlier. The novel features of SmartSifter are: (1) it is adaptive to non-stationary sources of data; (2) a score has a clear statistical/information-theoretic meaning; (3) it is computationally inexpensive; and (4) it can handle both categorical and continuous variables. An experimental application to network intrusion detection shows that SmartSifter was able to identify data with high scores that corresponded to attacks, with low computational costs. Further experimental application has identified a number of meaningful rare cases in actual health insurance pathology data from Australia's Health Insurance Commission.

outlier detectionintrusion detectionanomaly detectionfraud detectionfinite mixture modelEM algorithm

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Kenji Yamanishi
    • 1
  • Jun-ichi Takeuchi
    • 1
  • Graham Williams
    • 2
  • Peter Milne
    • 2
  1. 1.Internet Systems Research LaboratoriesNEC CorporationMiyamae, Kawasaki, KanagawaJapan
  2. 2.CSIRO Mathematical and Information SciencesCanberraAustralia