Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (19): 119-121.

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Research on advanced frequent itemsets mining algorithm

WANG Yan, LI Lingling, SHAO Xiaoyan   

  1. Department of Computer Science and Application, Zhengzhou Institute of Aeronautic Industry Management, Zhengzhou 450015, China
  • Online:2012-07-01 Published:2012-06-27

改进的频繁项集挖掘算法研究

王  艳,李玲玲,邵晓艳   

  1. 郑州航空工业管理学院 计算机科学与应用系,郑州 450015

Abstract: In view of the association rule mining technology and the research and analysis of its classic Apriori algorithm and FP-growth algorithm, an advanced frequent itemsets mining algorithm is proposed. The improved algorithm stores database using of matrix and calculates itemsets’ support number in terms of the matrix operation, which reduces the number of times for database scanning. The algorithm creates frequent pattern tree using of orderly frequent item adjacency matrix, which effectively reduces the branch and layer of the tree. Finally the examples analyze the frequent itemsets of mining process.

Key words: data mining, association rules, adjacency matrix, frequent pattern tree

摘要: 通过对关联规则挖掘技术及经典算法Apriori和FP-growth的研究和分析,提出了一种改进的频繁项集挖掘算法。该算法利用矩阵存储数据,并结合矩阵运算求项集的支持数,有效减少了事务数据库的扫描次数;利用有序频繁项目邻接矩阵创建频繁模式树,有效减少了频繁模式树的分支和层数。通过实例分析了频繁项集的挖掘过程。

关键词: 数据挖掘, 关联规则, 邻接矩阵, 频繁模式树