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Application of Feature Selection for Unsupervised Learning in Prosecutors’ Office

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Fuzzy Systems and Knowledge Discovery (FSKD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3614))

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Abstract

Feature selection is effective in removing irrelevant data. However, the result of feature selection in unsupervised learning is not as satisfying as that in supervised learning. In this paper, we propose a novel methodology ULAC (Feature Selection for Unsupervised Learning Based on Attribute Correlation Analysis and Clustering Algorithm) to identify important features for unsupervised learning. We also apply ULAC into prosecutors’ office to solve the real world application for unsupervised learning.

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Authors and Affiliations

  1. School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, P.R. China

    Peng Liu, Jiaxian Zhu, Lanjuan Liu, Yanhong Li & Xuefeng Zhang

Authors
  1. Peng Liu
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  2. Jiaxian Zhu
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  3. Lanjuan Liu
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  4. Yanhong Li
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  5. Xuefeng Zhang
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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Liu, P., Zhu, J., Liu, L., Li, Y., Zhang, X. (2005). Application of Feature Selection for Unsupervised Learning in Prosecutors’ Office. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_5

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  • DOI: https://doi.org/10.1007/11540007_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28331-7

  • Online ISBN: 978-3-540-31828-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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