关键词: 特征选择，聚类计算，部域距离，属性重要度

Abstract: To overcome the limitation of bad results on clustering and time-consuming of existing clustering algorithm to high-dimensional data, we provided an unsupervised feature selection algorithm based on neighborhood distance, then we clustered again on the selected feature subset The use of the selected feature subset can improve clustering accuracy. The results of the experiment show that the method can find the valid features, and also improve the timcconsuming problems in clustering on high-dimensional data.

Key words: Feature selection, Clustering compute, Neighborhood distance, Attribute significance