从树状数据库中挖掘频繁嵌入子树

Lizhi Liu, Jun Liu
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引用次数: 7

摘要

从标记树数据库中挖掘频繁子树是一个新兴的研究领域,在计算机网络、Web挖掘、生物信息学、XML文档挖掘等领域有着广泛的实际应用。这些应用程序都需要标记树具有更强的表达能力,以捕获数据实体之间的复杂关系。本文介绍了一种挖掘树状数据库中频繁、有序、嵌入子树的有效算法。该算法使用一种称为作用域列表(scope-list)的新数据结构(它是树节点的一种规范表示),首先生成所有候选树,然后枚举嵌入式有序树,最后连接作用域列表计算嵌入式有序树的频率。实验表明,该算法的性能比其他类似的挖掘方法提高了15%左右,并且具有良好的扩展性能。
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Mining Frequent Embedded Subtree from Tree-Like Databases
Mining frequent sub tree from databases of labeled trees is a new research field that has many practical applications in areas such as computer networks, Web mining, bioinformatics, XML document mining, etc. These applications share a requirement for the more expressive power of labeled trees to capture the complex relations among data entities. In this paper an efficient algorithm is introduced for mining frequent, ordered, embedded sub tree in tree-like databases. Using a new data structure called scope-list, which is a canonical representation of tree node, the algorithm first generates all candidate trees, then enumerates embedded, ordered trees, finally joins scope-list to compute frequency of embedded ordered trees. Experiments show the performance of the algorithm is about 15% better than other similar mining methods and has good scale-up properties.
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