一种不确定性数据中最大频繁项集挖掘方法

A new algorithm for mining maximal frequent itemsets from uncertain data

不确定性数据挖掘已经成为数据挖掘领域的新热点,频繁项集挖掘是重点研究的问题之一.但是目前出现的挖掘算法大多集中在完全频繁项集,而用于最大频繁项集和频繁闭项集的算法尚不多见.文中研究了一种基于UF-Tree的用于不确定性数据中挖掘最大频繁项集的算法,该挖掘过程分为两个步骤,第一步先得到以频繁1-项集为后缀的局部最大频繁项集,第二步得到所有的全局最大频繁项集,实验证明该算法性能良好且特别适用于稠密型、事务长度较小的数据集.

Recently,the research on uncertain data mining has become a new hotspot in the area of data mining, and the frequent itemsets mining is one of the focus issues. The existing algorithms mostly concentrated on the complete frequent itemsets, and there is few algorithms used to mine maximal or closet ones. This paper proposes a new algorithm UMF-growth to mine maximal fre- quent itemsets from uncertain data. The mining process of the UMF-growth is divided into two steps： the first step is to find out all of the local maximal frequent itemsets with the frequent 1-i- tem as suffixes, respectively. And the second step is to get all the maximal frequent itemsets. The experimental results show that the performance of UMF-growths is very good and especially suitable for the dense database.