关键词: 特征选择, 密度聚类算法, 文本聚类

Abstract: Test clustering is an unsupervised learning method. Some effective supervised feature selection methods can’t be applied to it because of the unavailability of class information. So a new feature selection algorithm base on class information was put forward in this paper, which addresses the Information Gain method on density clustering results to select out the best small part of features. And the results show the feasibility and effectiveness of the algorithm.

Key words: feature selection, density clustering algorithm, test clustering