Title: Unsupervised Feature Selection Method Based on K-means Clustering
Abstract: The first problem need to be solved in pattern recognition method is feature selection. Now many methods think more about supervised feature selection problem, but involve little about unsupervised feature selection problem. In this paper, a feature selection algorithm based on K-means clustering method is proposed involving classification capabilities of feature vectors and correlation analysis between two features. This method can be used in unsupervised feature selection problem.
Publication Year: 2005
Publication Date: 2005-01-01
Language: en
Type: article
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Cited By Count: 2
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