摘要
提出了一种基于频繁子树模式的GML文档结构聚类算法GCFS(GML Clustering based on Frequent Subtree patterns),与其他相关算法不同,该算法首先挖掘GML文档集合中的最大与闭合频繁Induced子树,并将其作为聚类特征,根据频繁子树的大小赋予不同的权值,采用余弦函数定义相似度,利用K-Means算法对聚类特征进行聚类。实验结果表明算法GCFS是有效的,具有较高的聚类效率,性能优于其他同类算法。
This paper presents algorithm GCFS for clustering GML document structure based on frequent subtree patterns.It firstly mines all maximal and closed frequent Induced subtrees from GML documents;then chooses some subtree patterns to form the clustering features,weights these features according to the length of subtree pattern,computes the similarity of two GML documents by cosine function,uses K-Means algorithm to cluster documents by clustering features.Experiment results show that GCFS is effective and efficient.Its performance is superior to other GML clustering algorithms.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第1期144-146,149,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.40871176~~