基于频繁项集的互补替代关系挖掘算法

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摘要以TOP-k-ClosedMiner算法为基础，提出基于索引的频繁项集挖掘算法Index-FIM。该算法用位向量表示数据集，同时引入广度扩展剪枝和区域索引跳过策略。实验表明，Index-FIM算法在稀疏数据集上挖掘频繁项集具有较高的执行效率。为得到能直接用于预测的有效信息，提出基于频繁项集的互补替代关系挖掘算法(CARM)。通过对已挖掘出的各频繁项集中的频繁项进行相关性计算，得到频繁项之间的互补替代关系，并以互补替代关系图(CAG)的形式直观表示，便于决策者做出准确、合理的判断。实验表明，CAG比频繁项集表示的信息更有效、更精确。

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	柴玉梅
	王春丽
	王黎明

关键词 ：数据挖掘, 频繁项集, 互补替代关系, 集合枚举树

Abstract：Based on the TOP-k-Closed Miner algorithm, an algorithm for mining frequent itemsets based on index (Index-FIM) is proposed. Bit-vector is used to represent dataset, the breadth expansion pruning strategy and region-index skimming strategy are simultaneously introduced. The experimental results show that Index-FIM has high execution efficiency for mining frequent itemsets in sparse datasets. In order to acquire useful information for direct prediction, another algorithm for complement-alternative relationship mining based on frequent itemsets (CARM) is proposed. The relevant coefficient between the items included in frequent itemsets is computed to get their complement-alternative relationship, and the complementary-alternative relationship is represented by complement-alternative relationship graph (CAG), which is convenient for the decision maker to make reasonable and accurate judgment. Experimental results show that CAG is more efficient and precise than frequent itemsets in expressing information.

Key words： Data Mining Frequent Itemsets Complement-Alternative Relationship Enumeration Tree

收稿日期: 2010-12-06

ZTFLH:

TP311

作者简介: 柴玉梅，女，1964年生，硕士，副教授，主要研究方向为机器学习与数据挖掘。E-mail:ieymchai@zzu。edu。cn。王春丽，女，1985年生，硕士，主要研究方向为数据挖掘。王黎明，男，1963年生，教授，博士生导师，主要研究方向为分布式人工智能与数据挖掘。

引用本文:

柴玉梅，王春丽，王黎明. 基于频繁项集的互补替代关系挖掘算法[J]. 模式识别与人工智能, 2012, 25(1): 157-165. CHAI Yu-Mei, WANG Chun-Li,WANG Li-Ming. An Algorithm for Mining Complement-Alternative Relationship Based on Frequent Itemsets. , 2012, 25(1): 157-165.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/ 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2012/V25/I1/157

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