基于Bagging的XML文档集成聚类研究  

Study of XML documents ensemble clustering based on Bagging

作　　者：赵斌[1] 张永胜[1] 

机构地区：[1]山东师范大学信息科学与工程学院,济南250014

出　　处：《计算机工程与应用》2009年第14期138-140,共3页Computer Engineering and Applications

基　　金：山东省自然科学基金No.Y2007G16;山东省青年科学家科研奖励基金(No.2006BS01020);山东省科技攻关计划No.2005GG4210002~~

摘　　要：将集成学习方法应用到XML文档聚类中来改进传统聚类算法的不足。提出一种标签与路径相结合的XML文档向量模型,基于这个模型,首先对原始文档集进行多次抽样,在新文档集上进行K均值聚类,然后对得到的聚类中心集合进行层次聚类。在人工数据集和真实数据集上的实验表明,该算法在召回率和精确率上优于K均值算法,并且增强了其鲁棒性。A method of ensemble learning is applied in XML documents clustering in order to improve the clustering performance.A novel vector model based on tag-path of XML documents is proposed and the documents are mapped to the model.The original datasets is sampled into several Bootstrap datasets,K-means algorithm is first run on each of the Bootstrap datasets,then hierarchical clustering algorithm is run on the sets of K-means clusters centers.The experimental result on the synthetic and real datasets shows that this algorithm is superior to the K-means algorithm on recall rate and precision rate,and enhances the robust of K-means algorithm.

关 键 词：集成学习 可扩展标记语言(XML) 文档聚类 BAGGING算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]