A Schema Feature Based Frequent Pattern Mining Algorithm for Semi-structured Data Stream

被引：0

作者：

Fu, Weiqi ^{[1
]}

Liao, Husheng ^{[1
]}

Jin, Xueyun ^{[1
]}

机构：

[1] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE 2017 5TH INTERNATIONAL CONFERENCE ON FRONTIERS OF MANUFACTURING SCIENCE AND MEASURING TECHNOLOGY (FMSMT 2017) | 2017年 / 130卷

基金：

北京市自然科学基金; 中国国家自然科学基金;

关键词：

frequent pattern mining; semi-structured data stream; schema feature;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Data mining is used to find useful information from massive data. Frequent pattern mining is one important task of data mining. Recently, the researches on frequent pattern mining for semi-structured data have made some progresses, and it also have a lot of focuses for data stream. However, only a few studies focus on both semi-structured data and data stream. This paper proposes an algorithm named SPrefixTreeISpan. We segment the semi-structured data stream first, and then uses the pattern-growth method to mine each segment. In the end, we maintain all the results on a structure called patternTree. At the same time, the mining algorithm is optimized by the inevitable parent-child relationship and the inevitable child-parent relationship extracted from XML schema. Experiment shows that SPrefixTreeISpan has better performance.

引用

页码：1329 / 1336

页数：8

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