基于频繁项集的互补替代关系挖掘算法

以TOP-k-Closed Miner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.

An Algorithm for Mining Complement-Alternative Relationship Based on Frequent Itemsets

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.