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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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
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