【摘要】 由于挖掘密集型数据的频繁模式完全集非常困难 ,因而改进了传统的FP -树结构并提出了一种基于改进FP -树的最大频繁模式挖掘算法IFP -MAX ;通过引入后缀子树的概念 ,在挖掘过程中不用生成最大频繁模式候选集 ,大大提高了算法的时空效率。实验表明 ,IFP -MAX的挖掘速度比Miafia和GenMax快得多更多还原

【Abstract】 Because mining complete set of frequent patterns from dense database could be impractical, in this paper, the structure of a traditional FP-tree is improved and an algorithm (called IFP-MAX) for mining maximal frequent patterns based on improved FP-tree is proposed.By introducing the concept of postfix sub-tree, the proposed algorithm needn’t generate the candidate of maximal frequent patterns in mining process and therefore greatly improves the mining efficiency in time and space scalability.Experimental results show that IFP-MAX is much faster than Mafia and GenMax.更多还原