期刊文献+

基于XML数据的关联规则挖掘研究 被引量:3

Study on mining association rules from XML data
下载PDF
导出
摘要 XML凭借其诸多优点,在短短的时间内迅速成为表示和交换信息的标准。大量XML数据的涌现给数据挖掘提出了新的挑战。传统关联规则挖掘是基于关系数据库的,因此现有许多XML数据关联规则挖掘的方法都或多或少地利用关系数据库—即把XML数据文档映射成关系数据库来完成的。在仔细研究了XML数据的访问接口后,给出了一个基于Apriori算法可直接从XML文档挖掘关联规则的类接口,并且在.NET平台下用C#语言实现了。 Due to its many advantages, XML (extensible markup language) has become as a standard for representing and exchanging information. The large amount of XML data emerges and a new challenge to data mining is given. Although there have been many methods to handle XML documents, these methods rely on the traditional relational databases more or less, i.e, mapping the XML documents into relational dataes. After carefully studying the XML data access class interface, a class interface for mining association rules from native XML data based on Apriori algorithm is given and implemented using C# in the.NET framework.
出处 《计算机工程与设计》 CSCD 北大核心 2006年第24期4704-4706,共3页 Computer Engineering and Design
关键词 数据挖掘 关联规则 可扩展标记语言 APRIORI .NET data mining association rules XML apriori ,net
  • 相关文献

参考文献14

二级参考文献58

  • 1孙淑玲.代数结构[M].合肥:中国科技大学出版社,1990..
  • 2Agrawa lR, Imielinski T, Swami A. Mining association rules between sets of items in large databases (C). In: Buneman P, Jajodia S,eds. Proc. of the ACM SIGMOD Conf. on Management of Data (SIGMOD'93). New York: ACM Press, 1993. 207~216.
  • 3Agrawa lR, Srikant R. Fast algorithms for mining association rules in large databases. In: Bocca JB, Jarke M, Zaniolo C, eds. Proc. of the 20th Int'l Conf. on Very Large Data Bases. Santiago: Morgan Kaufmann, 1994. 478~499.
  • 4Aly HH, Taha Y, Amr AA. Fast mining of association rules in large-scale problems. In: Abdel-Wahab H, Jeffay K, eds. Proc. of the 6th IEEE Symp. on Computers and Communications (ISCC 2001). New York: IEEE Computer Society Press, 2001. 107~113.
  • 5Tsai CF, Lin YC, Chen CP. A new fast algorithms for mining association rules in large databases. In: Kamel AE, Mellouli K, Borne P, eds. Proc. Of the 2002 IEEE Int'l Conf. On Systems, Man and Cybernetics (SMC 2002). IEEE Computer Society Press, 2002. 251
  • 6Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. In: Chen WD, Naughton J, Bernstein PA, eds. Proc. of the 2000 ACM SIGMOD Int'l Conf. on Management of Data (SIGMOD 2000). New York: ACM Press, 2000. 1~12.
  • 7Han JW, Kember M. Data Mining. Concepts and Techniques. 2nd ed. Beijing: Higher Education Press, 2001. 240-243.
  • 8Zaki MJ. Scalable algorithms for association mining. IEEE Trans. on Knowledge and Data Engineering, 2000,12(3):372-390.
  • 9Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases [C]. Proceedings of the ACM SIGMOD conference on management of data, 1993, 207-216.
  • 10Han J, Pei J, Yin Y. Mining frequent pattems without candidate generation [C]. Proc 2000 ACM-SIGMOD Int Conf Management of Data(SIGMOD' 00), Dalas, TX, 2000.

共引文献448

同被引文献19

引证文献3

二级引证文献4

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部