基于词条与语意差异度量的文档聚类算法

已有的文本聚类算法大多基于一般的相似性度量而忽略了语义内容,对此提出一种基于最大化文本判别信息的文本聚类算法。首先,分别分析词条对其类簇与其他类簇的判别信息,并且将数据集从输入空间转换至差异分数矩阵空间；然后,设计了一个贪婪算法来筛选矩阵每行的低分数词条；最终,采用最大似然估计对文本差别信息进行平滑处理。仿真实验结果表明,所提方法的文档聚类质量优于其他分层与单层聚类算法,并且具有较好的可解释性与收敛性。

Term and Semantic Difference Metric Based Document Clustering Algorithm

The existing document clustering algorithms are based on the common similarity measurement,but ignore the semantics.So a document clustering algorithm based on maximizing the sum of the discrimination information provided by documents was proposed.Firstly,the discrimination information of term for the corresponding cluster and for the other clusters was analyzed separately,and the data set was transformed from input space to the difference scores matrix space.Then a greedy algorithm was designed to filter the terms with low score from each row of the matrix.Lastly,maximum likelihood estimation was used to smooth the document difference information.Simulation experiment results show that the proposed method has better cluster quality than the plat and hierarchical clustering algorithms,and has a good quality in interpretability and convergence.