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Local Subspace Based Outlier Detection

  • Ankur Agrawal
Part of the Communications in Computer and Information Science book series (CCIS, volume 40)

Abstract

Existing studies in outlier detection mostly focus on detecting outliers in full feature space. But most algorithms tend to break down in high-dimensional feature spaces because classes of objects often exist in specific subspace of the original feature space. Therefore, subspace outlier detection has been recently defined. As a novel solution to tackle this problem, we propose here a local subspace based outlier detection technique, which uses different subspaces for different objects. Using this concept we adopt local density based outlier detection to cope with high-dimensional data. A broad experimental evaluation shows that this approach yields results of significantly better quality than existing algorithms.

Keywords

Outlier detection Local subspace 

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References

  1. 1.
    Yue, D., Wu, X., Wang, Y., Li, Y., Chu, C.: A Review of Data Mining-Based Financial Fraud Detection Research. In: 2007 International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai, P. R. China, pp. 5514–5517 (2007)Google Scholar
  2. 2.
    Zhang, J., Zulkernine, M.: Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection. In: 2006 IEEE International Conference on Communications, Istanbul, Turkey, pp. 2388–2393 (2006)Google Scholar
  3. 3.
    Podgorelec, V., Heri_ko, M., Rozman, I.: Improving Mining of Medical Data by Outliers Prediction. In: 18th IEEE International Symposium on Computer-Based Medical Systems, Ireland, pp. 91–96 (2005)Google Scholar
  4. 4.
    Näsi, J., Sorsa, A., Leiviskä, K.: Sensor Validation And Outlier Detection Using Fuzzy Limits. In: 44th IEEE Conference on Decision and Control, and the European Control Conference, Seville, Spain, pp. 7828–7833 (2005)Google Scholar
  5. 5.
    Hawkins, D.: Identification of Outliers. Chapman and Hall, London (1980)CrossRefGoogle Scholar
  6. 6.
    Hodge, V., Austin, J.: A Survey of Outlier Detection Methodologies. Artificial Intelligence Review, 85–126 (2004)Google Scholar
  7. 7.
    Eskin, E.: Anomaly Detection over Noisy Data Using Learned Probability Distributions. In: 17th International Conference on Machine Learning, Stanford, CA, USA, pp. 255–262 (2000)Google Scholar
  8. 8.
    Yamanishi, K., Takeuchi, J.: Discovering Outlier Filtering Rules from Unlabeled Data-Combining a supervised Learner with an Unsupervised Learner. In: 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, pp. 389–394 (2001)Google Scholar
  9. 9.
    Knorr, E., Ng, R.: Algorithms for mining distance-based outliers in large datasets. In: 24th International Conference on Very Large Data Bases, San Francisco, CA, USA, pp. 392–403 (1998)Google Scholar
  10. 10.
    Breunig, M., Kriegel, H., Ng, R., Sander, J.: LOF: identifying density-based local outliers. In: SIGMOD 2000 International Conference on Management of Data, Dallas, Texas, USA, pp. 93–104 (2000)Google Scholar
  11. 11.
    Hinneburg, A., Aggarwal, C., Keim, D.: What is the Nearest Neighbor in High Dimensional Spaces. In: 26th International Conference on Very Large Databases, Cairo, Egypt, pp. 506–515 (2000)Google Scholar
  12. 12.
    Cao, H., Si, G., Zhu, W., Zhang, Y.: Enhancing Effectiveness of Density-based Outlier Mining. In: 2008 International Symposiums on Information Processing. Moscow, pp. 149–154 (2008)Google Scholar
  13. 13.
    Nguyen, M., Mark, L., Omiecinski, E.: Subspace Outlier Detection in Data with Mixture of Variances and Noise. Report Number GT-CS-08-11, Georgia Institute of Technology, Atlanta, GA 30332, USA (2008)Google Scholar
  14. 14.
    Newman, C., Merz, C.: UCI repository of machine learning databases (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ankur Agrawal
    • 1
  1. 1.G.L.A. Institute of Technology and Management, MathuraMathuraIndia

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