Efficient algorithms for finding frequent substructures from semi-structured data streams

被引：0
|
作者：
Asai, Tatsuya [1 ]
Abe, Kenji [1 ]
Kawasoe, Shinji [1 ]
Arimura, Hiroki [1 ]
Arikawa, Setsuo [1 ]
机构：
[1] Kyushu Univ, Higashi Ku, 6-10-1 Hakozaki, Fukuoka 8128581, Japan
来源：
NEW FRONTIERS IN ARTIFICIAL INTELLIGENCE | 2007年 / 3609卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, we present an online algorithm StreamT that receives fragments of an unseen possibly infinite semi-structured data in the document order through a data stream, and can return the current set of frequent patterns immediately on request at any time. We give modifications of the algorithm to other online mining models. Furthermore we implement our algorithms in different online models and candidate management strategies, then show empirical analyses to evaluate the algorithms.
引用
收藏
页码：29 / +
页数：3
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