摘要
在实际应用中,XML(eXtensible Markup Language)文档中的一些结构经常被改变。为了挖掘XML文档在历史变化过程中经常改变的结构所蕴含的知识,提出了发现频繁变化结构的方法。该方法用一组频繁变化结构组成的文档向量模型代表一个XML文档,将频繁变化结构在簇中的出现比例作为权值,使用加权余弦相似度对XML文档进行聚类。经过实验分析,根据XML文档历史变化过程中的频繁变化结构能较好地将XML文档进行聚类。用加权余弦相似度对XML文档进行聚类,其聚类结果的正确率、召回率和簇内部距离均优于使用非加权余弦相似度对XML文档进行聚类得到的结果。
In practical applications,some structures of an XML （eXtensible Markup Language） document are often changed. In order to mining knowledge hiden in the freduently changing structures in the XML document history changes,a method to found the frequently changing structures is proposed,then uses a document-vector model which composition by a set of frequently changing structures to represent an XML document,to proportion that frequently changing structures appearance in the cluster as weight,and cluster XML documents use weighted cosine similarity. After experimental analysis,according to frequently changing structures which found in the XML document historical change process will be better able to cluster XML documents. Cluster XML document using the weighted cosine similarity,the precision rate,recall rate and cluster internal distance of cluster result are all better than the results obtained by use non-weighted cosine similarity.
出处
《吉林大学学报（信息科学版）》
CAS
2010年第1期68-76,共9页
Journal of Jilin University（Information Science Edition）
基金
吉林省科技发展计划基金资助项目(20090704)
关键词
XML文档聚类
加权余弦相似度
频繁变化结构
XML document clustering
weighted cosine similarity
frequently changing structures
