Outlier Detection Using Replicator Neural Networks

  • Simon Hawkins
  • Hongxing He
  • Graham Williams
  • Rohan Baxter
Conference paper
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    D. H. Ackley, G. E. Hinton, and T. J. Sejinowski. A learning algorithm for boltzmann machines. Cognit. Sci., 9:147–169, 1985.CrossRefGoogle Scholar
  2. [2]
    A. C. Atkinson. Fast very robust methods for the detection of multiple outliers. Journal of the American Statistical Association, 89:1329–1339, 1994.MATHCrossRefGoogle Scholar
  3. [3]
    A. Bartkowiak and A. Szustalewicz. Detecting multivariate outliers by a grand tour. Machine Graphics and Vision, 6(4):487–505, 1997.Google Scholar
  4. [4]
    M. Breunig, H. Kriegel, R. Ng, and J. Sander. Lof: Identifying density-based local outliers. In Proc. ACM SIGMOD,Int. Conf. on Management of Data, 2000.Google Scholar
  5. [6]
    W. DuMouchel and M. Schonlau. Afast computer intrusion detection algorithm based on hypothesis testing of command transition probabilities. In Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining, pages 189–193, 1998.Google Scholar
  6. [7]
    M. Ester, H. P. Kriegel, J. Sander, and X. Xu. Adensit y-based algorithm for discovering clusters in large spatial databases with noise. In Proc. KDD, pages 226–231, 1999.Google Scholar
  7. [8]
    T. Fawcett and F. Provost. Adaptive fraud detection. Data Mining and Knowledge Discovery Journal, 1(3):291–316, 1997.CrossRefGoogle Scholar
  8. [9]
    D. M. Hawkins. Identification of outliers. Chapman and Hall, London, 1980.MATHGoogle Scholar
  9. [10]
    R. Hecht-Nielsen. Replicator neural networks for universal optimal source coding. Science, 269(1860–1863), 1995.CrossRefGoogle Scholar
  10. [11]
    E. Knorr and R. Ng. A unified approach for mining outliers. In Proc. KDD, pages 219–222, 1997.Google Scholar
  11. [12]
    E. Knorr and R. Ng. Algorithms for mining distance-based outliers in large datasets. In Proc. 24th Int. Conf. Very Large Data Bases,VLDB, pages 392–403, 24-27 1998.Google Scholar
  12. [13]
    E. Knorr., R. Ng, and V. Tucakov. Distance-based outliers: Algorithms and applications. VLDB Journal: Very Large Data Bases, 8(3–4):237–253, 2000.CrossRefGoogle Scholar
  13. [14]
    George Kollios, Dimitrios Gunopoulos, Nick Koudas, and Stefan Berchtold. An efficient approximation scheme for data mining tasks. In ICDE, 2001.Google Scholar
  14. [15]
    A. S. Kosinksi. A procedure for the detection of multivariate outliers. Computational Statistics and Data Analysis, 29, 1999.Google Scholar
  15. [16]
    R. Ng and J. Han. Efficient and effiective clustering methods for spatial data mining. In Proc. 20th VLDB, pages 144–155, 1994.Google Scholar
  16. [17]
    S. Ramaswamy, R. Rastogi, and K. Shim. Efficient algorithms for mining outliers from large data sets. In Proceedings of International Conference on Management of Data,A CM-SIGMOD, Dallas, 2000.Google Scholar
  17. [18]
    D. F. Swayne, D. Cook, and A. Buja. XGobi: interactive dynamic graphics in the X window system with a link to S. In Proceedings of the ASA Section on Statistical Graphics, pages 1–8, Alexandria, VA, 1991. American Statistical Association.Google Scholar
  18. [19]
    P. Sykacek. Equivalent error bars for neural network classifiers trained by bayesian inference. In Proc. ESANN, 1997.Google Scholar
  19. [20]
    G. Williams, I. Altas, S. Bakin, Peter Christen, Markus Hegland, Alonso Marquez, Peter Milne, Rajehndra Nagappan, and Stephen Roberts. The integrated delivery of large-scale data mining: The ACSys data mining project. In Mohammed J. Zaki and Ching-Tien Ho, editors, Large-Scale Parallel Data Mining, LNAI State-of-the-Art Survey, pages 24–54. Springer-Verlag, 2000.Google Scholar
  20. [21]
    G. Williams and Z. Huang. Mining the knowledge mine: The hot spots methodology for mining large real world databases. In Abdul Sattar, editor, Advanced Topics in Artificial Intelligence, volume 1342 of Lecture Notes in Artificial Intelligenvce, pages 340–348. Springer, 1997.Google Scholar
  21. [22]
    K. Yamanishi, J. Takeuchi, G. Williams, and P. Milne. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithm. In Proceedings of KDD2000, pages 320–324, 2000.Google Scholar
  22. [23]
    T. Zhang, R. Ramakrishnan, and M. Livny. An efficient data clustering method for very large databases. In Proc. ACM SIGMOD, pages 103–114, 1996.Google Scholar

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

Personalised recommendations