机构地区: 西北师范大学数学与信息科学学院
出 处: 《计算机工程》 2011年第24期161-163,共3页
摘 要: 提出一种基于非负矩阵分解(NMF)的双重约束文本聚类算法。在正交三重NMF模型中,加入文本空间的成对约束信息和词空间的类别约束信息,将不同的特征词项进行分类。利用迭代规则对原始的词-文档矩阵进行分解,获得文本聚类结果。与多种传统半监督文本聚类算法的对比结果表明,该算法具有较高的聚类精度,能提供更准确和有效的聚类结果。 Non-negative Matrix Factorization(NMF) with dual constraints method for document clustering is proposed. It is based on NMF model with adding of pair-wise constraints on documents and categorization constraints of the words. Iterative rules obtained from the original word-document matrix are decomposed to get document clustering results. Compared with a variety of popular semi-supervised clustering algorithm, the method Ibr document clustering can effectively improve the accuracy of document clustering, and can provide more accurate and efficient clustering results.
领 域: [自动化与计算机技术] [自动化与计算机技术]