一种基于结构信息总结树的XML文档聚类方法

Clustering XML Documents Based on a Structural Summary Tree

提出一种有效的XML文档结构信息表达方法,用数字化的结构总结树SST对XML文档的结构信息进行编码,在此基础上给出结构距离的定义,并采用遗传算法对XML文档进行聚类.实验证明该方法分类准确率高,易于实现,且不需先验的DTD知识.

An approach for calculating the structural similarity between XML documents is proposed in this paper. The structural information of an XML document is captured with a structural summary tree (SST). By encoding elements as digital numbers, a SST is transformed to a digit-labeled tree. Digital numbers at different tree levels are concatenated to form a vector after the normalization process. Consequently, each XML document is represented as an m-dimension vector. The GA-based clustering algorithm is adopted since it is able to provide good results irrespective of the starting configuration. Experimental results show the effectiveness and scalability of the approach.