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A Unified Subspace Outlier Ensemble Framework for Outlier Detection

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Advances in Web-Age Information Management (WAIM 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3739))

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Abstract

This paper proposes a unified framework for outlier detection in high dimensional spaces from an ensemble-learning viewpoint. Moreover, to demonstrate the usefulness of our framework, we developed a very simple and fast algorithm, namely SOE1, in which only subspaces with one dimension is used for mining outliers from large categorical datasets. Experimental results demonstrate the superiority of SOE1 algorithm.

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References

  1. He, Z., Xu, X., Huang, J., Deng, S.: A Frequent Pattern Discovery Based Method for Outlier Detection. In: Li, Q., Wang, G., Feng, L. (eds.) WAIM 2004. LNCS, vol. 3129, pp. 726–732. Springer, Heidelberg (2004)

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Harbin Institute of Technology, China

    Zengyou He, Shengchun Deng & Xiaofei Xu

Authors
  1. Zengyou He
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  2. Shengchun Deng
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  3. Xiaofei Xu
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Editor information

Editors and Affiliations

  1. University of Edinburgh & Bell Laboratories,  

    Wenfei Fan

  2. College of Computer Science, Zhejiang University, 310027, Hangzhou, Zhejiang, China

    Zhaohui Wu

  3. Dept. of E. I. E, Huazhong University of Science and Technology, Wuhan, China

    Jun Yang

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© 2005 Springer-Verlag Berlin Heidelberg

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He, Z., Deng, S., Xu, X. (2005). A Unified Subspace Outlier Ensemble Framework for Outlier Detection. In: Fan, W., Wu, Z., Yang, J. (eds) Advances in Web-Age Information Management. WAIM 2005. Lecture Notes in Computer Science, vol 3739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11563952_56

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  • DOI: https://doi.org/10.1007/11563952_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29227-2

  • Online ISBN: 978-3-540-32087-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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