On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms

Kenji Yamanishi, Jun Ichi Takeuchi, Graham Williams, Peter Milne

研究成果: ジャーナルへの寄稿学術誌査読

290 被引用数 (Scopus)


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.

ジャーナルData Mining and Knowledge Discovery
出版ステータス出版済み - 5月 2004

!!!All Science Journal Classification (ASJC) codes

  • 情報システム
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信


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