Application of feature selection for unsupervised learning in prosecutors' office

Authors:
Peng Liu;Jiaxian Zhu;Lanjuan Liu;Yanhong Li;Xuefeng Zhang
Affiliations:
School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, P.R. China;School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, P.R. China;School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, P.R. China;School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, P.R. China;School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, P.R. China
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Year:
2005

Citing 5
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Feature Selection for Unsupervised Learning

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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.