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Outlier Detection Using Replicator Neural Networks

  • Simon Hawkins
  • Hongxing He
  • Graham Williams
  • Rohan Baxter
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2454)

Abstract

We consider the problem of finding outliers in large multivariate databases. Outlier detection can be applied during the data cleansing process of data mining to identify problems with the data itself, and to fraud detection where groups of outliers are often of particular interest. We use replicator neural networks (RNNs) to provide a measure of the outlyingness of data records. The performance of the RNNs is assessed using a ranked score measure. The effectiveness of the RNNs for outlier detection is demonstrated on two publicly available databases.

Keywords

Hide Layer Outlier Detection Fraud Detection Network Intrusion Detection Outlier Detection Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Simon Hawkins
    • 1
  • Hongxing He
    • 1
  • Graham Williams
    • 1
  • Rohan Baxter
    • 1
  1. 1.CSIRO Mathematical and Information SciencesCanberraAustralia

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