一种基于邻域距离的聚类特征选择方法

针对高维复杂的符号数据集在聚类中的聚类效果差和计算耗时过大的问题,首先提出了一种基于邻域距离的无监督特征选择算法,然后在选择到的特征子集上进行重新聚类,从而有效提高了聚类结果的精度,降低了聚类计算的计算耗时。实验结果表明,该算法可以找到有效的特征子集,提高数据集的聚类精度,降低面对高维复杂数据集聚类的计算耗时。

Clustering Feature Selection Method Based on Neighborhood Distance

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.