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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 make a comprehensive overview of existing methods of feature selection in unsupervised learning and 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 and practical data mining framework into a prosecution committee to solve the real world application for unsupervised learning.