基于FP-Tree的反向频繁项集挖掘

在拓展现有反向频繁挖掘问题定义,探索反向频繁项集的3个具体应用后,提出了一种基于FP-tree的反向频繁项集挖掘方法.该方法首先采用分治思想,将目标约束划分为若干子约束,每步求解一个子线性约束问题,经过若干步迭代后找到一个满足整个给定约束的目标FP-tree;然后根据目标FP-tree生成一个仅含频繁项的临时事务数据库TempD;最后通过向TempD中撒入非频繁项得到目标数据集.理论分析和实验表明该方法是正确的、高效的,且与现有方法仅能输出1个目标数据集相比,该方法能够输出较多的目标数据集.

Inverse Frequent Itemset Mining Based on FP-Tree

After the current definition of the inverse frequent set mining problem is expanded and its three practical applications are explored, an FP-tree-based method is proposed for the inverse mining problem. First, the method divides target constraints into some sub constraints and each time it solves a sub linear constraint problem. After some iterations, it finds an FP-tree satisfying the whole given constraints. Then, based on the FP-tree it generates a temporary database TempD that only involves frequent items. The target datasets are obtained by scattering infrequent items into TempD. Theoretic analysis and experiments show that the method is right and efficient. Moreover, compared with the current methods, the method can output more than one target data set.