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Application of Feature Selection for Unsupervised Learning in Prosecutors’ Office

  • Peng Liu
  • Jiaxian Zhu
  • Lanjuan Liu
  • Yanhong Li
  • Xuefeng Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Peng Liu
    • 1
  • Jiaxian Zhu
    • 1
  • Lanjuan Liu
    • 1
  • Yanhong Li
    • 1
  • Xuefeng Zhang
    • 1
  1. 1.School of Information Management and EngineeringShanghai University of Finance and EconomicsShanghaiP.R. China

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