一种高效的用于文本聚类的无监督特征选择算法

特征选择虽然非常成功地应用于文本分类，但却很少用于文本聚类，这是因为那些高效的特征选择方法通常都是有监督的特征选择算法，它们因为需要类信息而无法直接应用于文本聚类.为了能将这些方法应用到文本聚类上，提出了一种新的无监督特征选择算法：基于K-Means的特征选择算法(KFS).这个算法通过在不同K-Means聚类结果上使用有监督特征选择的方法，成功地选择出了最为重要的一小部分特征，使文本聚类的性能提高了近15%.

An Effective Unsupervised Feature Selection Method for Text Clustering

Feature selection has been successfully applied to text categorization, but rarely applied to text clustering, because those effective supervised feature selection methods can't be applied to text clustering due to the unavailability of class label information. So a new feature selection method called "K-Means based feature selection (KFS)" method is proposed in this paper, which addresses the unavailability of label information by performing effective supervised feature selections on different K-Means clustering results. Experimental results show that (1) KFS successfully selects out the best small part of features and significantly improves the clustering performance; and (2) Compared with other feature selection methods, KFS is very close to the ideal supervised feature selection methods and much better than any unsupervised methods.