北京航空航天大学学报 ›› 2004, Vol. 30 ›› Issue (08): 791-796.

• 论文 • 上一篇    下一篇

一种频繁复合项目集的混合求解方法

李国和1, 赵沁平1, 王喜2   

  1. 1. 北京航空航天大学 计算机学院, 北京 100083;
    2. 北京城市学院, 北京 100083
  • 收稿日期:2003-04-02 出版日期:2004-08-31 发布日期:2010-09-21
  • 作者简介:李国和(1965-),男,福建漳州人,博士生, guohe li@sina.com.

Synthesizing algorithm for mining composite-frequent item sets

Li Guohe1, Zhao Qinping1, Wang Xi2   

  1. 1. School of Computer Science and Technology, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
    2. Beijing City College, Beijing 100083, China
  • Received:2003-04-02 Online:2004-08-31 Published:2010-09-21

摘要: 关联规则挖掘的关键在于频繁项目集的求解,为了能够在含有数值类型数据的交易数据库中快速求解含有多值的频繁项目集,拓展了含有多种数值的交易数据库定义.在此基础上,根据树的思想,建立含有交易项和交易数量的树,并结合Apriori算法和智能搜索,提出在各个较小的树枝路径中求解频繁项目集求解方法FABCTA(Fast Algorithm ByCandidate Transaction Tree and Apriori).通过采用真实数据实验对比,FABCTA效率明显优于Apriori算法.

Abstract: It is very important to get the frequent item set in the associate rule mining. In order to fast obtain the frequent item set from a database that includes multiple values, the definition of transaction database was extended. And then by the tree concept, a special tree was built in which every node is formed by item and item’s count. At last, on the foundation of Apriori Algorithm and Artificial Intelligent Search, FABCTA(fast algorithm by candidate transaction tree and apriori) was presented to solve the frequent item set in small branches of tree. By the test on real data, FABCTA is more efficient than Apriori algorithm.

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