XML文档的鲁棒聚类方法

Bin Zhao, Yong-sheng Zhang, Huaxiang Zhang
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引用次数: 2

摘要

随着Internet上XML数据的不断增加,对海量XML文档的管理和分析已成为信息管理的重要内容。本文讨论了XML文档的聚类问题。借鉴半聚类的思想,提出了一种将单分区聚类算法与分层聚类算法相结合的鲁棒聚类方法,消除了单一聚类算法的缺陷。实际的XML文档收集实验表明,该方法可以在不固定簇数的情况下,将大量XML文档有效地分组到合适的簇中。与单一聚类算法相比,该方法对噪声的敏感性较低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Robust Clustering Method for XML Documents
With the increase of XML data over the Internet, managing and analyzing huge amount of XML documents has played an important role for information management. This paper addresses the problem of clustering XML documents. Borrowing the idea of semi-clustering, it proposes a robust clustering method through a combination of single partitional and hierarchical clustering algorithms, which can eliminate the defects of single clustering algorithms. Experiments on real XML documents collection show that our method can group large collection of XML documents into appropriate clusters efficiently without fixed number of clusters. Moreover, our method is less sensitive to noises than single clustering algorithm.
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