多层扩展挖掘最大频繁项集

Mining Maximal Frequent Item Sets Based on Multilevel Extension

本文提出一种新的搜索最大频繁项集的算法。该算法使用多层扩展深度优先搜索方法,结合有效的前瞻剪枝策略,明显加速了最大频繁项集的生成,从而显著地降低了CPU时间。

We present KMAX, a new depth-first search algorithm for mining maximal frequent itemsets. KMAX uses a novel technique called multilevel extension to extend the items in the search tree with an efficient look-ahead pruning method to prune the search space. Experimental comparison with the previous work indicates that it obviously accelerates the generation of maximal frequent itemsets , therefore the CPU time is reduced remarkably.