摘要： 为有效解决文档聚类问题，提出一种基于间隔流形学习的文档聚类算法。该算法利用间隔Fisher分析将高维文档空间降维到低维特征空间，利用支持向量聚类算法进行聚类。在基准文档测试集上的实验结果表明，该算法的聚类性能优于其他常用的文档聚类算法。

关键词: 文档聚类, 流形学习, 支持向量聚类, 数据挖掘

Abstract: To effectively deal with the document clustering problem, a novel document clustering algorithm based on marginal manifold learning is proposed. The high dimensional document space is reduced into the lower dimensional feature space with marginal fisher analysis. The support vector clustering algorithm is applied to cluster documents herein. Experimental results on the benchmark document sets show the algorithm achieves much better clustering performance than tradition document clustering algorithms.

Key words: document clustering, manifold learning, support vector clustering, data mining

中图分类号: 

• TP181