作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程 ›› 2012, Vol. 38 ›› Issue (01): 59-61. doi: 10.3969/j.issn.1000-3428.2012.01.015

• 软件技术与数据库 • 上一篇    下一篇

基于图和双向搜索的频繁项集挖掘算法

刘 芳   

  1. (重庆邮电大学计算机学院,重庆 400065)
  • 收稿日期:2011-06-10 出版日期:2012-01-05 发布日期:2012-01-05
  • 作者简介:刘 芳(1986-),女,硕士研究生,主研方向:数据挖掘
  • 基金资助:
    教育部留学回国人员科研启动基金资助项目(教外司留[2007]1108号);重庆邮电大学科研基金资助项目(A2006-05)

Frequent Itemset Mining Algorithm Based on Graph and Two-directional Search

LIU Fang   

  1. (College of Computer, Chongqing University of Post and Telecommunications, Chongqing 400065, China)
  • Received:2011-06-10 Online:2012-01-05 Published:2012-01-05

摘要: 基于图的关联规则挖掘算法会产生大量候选项集。针对该问题,提出一种结合双向搜索策略的改进算法。按照支持度对频繁 1-项集排序,对频繁k-项集的最长超集进行验证,利用Apriori算法进行剪枝。实验结果表明,在支持度阈值较小时,改进算法能有效减少候选项集的数量,提高挖掘效率。

关键词: 频繁项集, 双向搜索, 关联图, 关联规则, 数据挖掘

Abstract: For discovering association rules based on graph can generate a large number of candidate itemsets, an improved algorithm is proposed. The improved algorithm combined the top-down and bottom-up search in all search process, and sorted the frequent 1-itemset on support degree and count the support of maximal superset of frequent k-itemsets. It utilizes direct graph and Apriori property to prune the redundant candidate itemsets. Experimental result shows that the improved algorithm reduce the number of candidate itemsets when the minimum support is small and the performance is improved.

Key words: frequent itemset, two-directional search, association graph, association rule, data mining

中图分类号: