Application of Feature Selection for Unsupervised Learning in Prosecutors’ Office
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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