基于改进FP-树的最大项目集挖掘算法

Maximum frequent itemsets mining algorithm based on improved FP-tree

挖掘最大频繁项目集是多种数据挖掘应用中的关键问题。FP-growth算法是目前最有效的频繁模式挖掘算法之一,其在挖掘最大项目集时要递归生成大量的条件FP-树,存在时空效率不高的问题。于是结合改进的FP-树,提出了一种快速挖掘最大项目集的算法。该算法利用改进的FP-树是单向的且每个节点只保留指向父节点的指针,可以节约大量的存储空间;同时引入项目序列集和它的基本操作,使挖掘最大频繁项目集时不生成含大量候选项目的集合或条件FP-树,可以快速地挖掘出所有的最大频繁项目集。实例分析证明所提出的算法是可行的。

Mining maximum frequent itemsets is a key problem in many data mining application.FP-growth algorithm is one of the most efficient frequent pattern mining methods.However,FP-growth algorithm must generate a huge number of conditional FP-trees recursively in processes of mining maximum frequent,so the efficiency of it unsatisfactory.This paper proposed an efficient mining maximum frequent algorithm,it unified the improvement FP-tree.The FP-tree was a one-way tree and there is no pointers to point its childre...