一种关联规则增量更新算法

An algorithm for updating frequent itemsets

关联规则是数据挖掘领域的一个重要分支,而发现频繁项目集是关联规则数据挖掘中的关键问题。频繁项目集是在给定的交易数据库D下满足最小支持度和最小置信度下的一个项目集合,但随着数据集的增减,就会产生不同的频繁项目集。如何发现在数据集变化情况下频繁项目集快速和高效地更新是文中解决的问题。为此提出了一种改进的增量更新算法,实验结果表明此算法有较好的效果。

Association rules is an important branch of data mining domain. Discovering frequent itemsets is a key problem in data mining association rules. The frequent itemsets is a set of all items that satisfies a minimum support and a minimum confidence in a given transactional database D. With the addition and subtraction of the data sets, there are the different frequent itemsets. How to find out the frequent itemsets fast and efficiently under the variance of data sets is a goal of the paper. And this paper gives an improved updating algorithm; an experiment result was given to demonstrate this algorithm has better effect.