一种新的动态频繁项集挖掘方法

New method for dynamic itemset mining

频繁项集挖掘是关联规则挖掘的重要步骤。在数据动态变化的环境下进行关联规则挖掘具有重要的现实意义。提出一种动态频繁项集挖掘算法,该算法建立在前一阶段挖掘的基础上,能避免过多地扫描数据库而影响挖掘性能,在最后生成全局频繁项集时,不需要全程扫描数据库,根据之前挖掘结果有选择地扫描相关的事务子集。实验表明,该算法挖掘性能远远优于Apriori算法,能有效地实现在数据动态变化环境下的挖掘频繁项集。

Mining frequent iteinsets is an important step in the association rules discovering.Mining the association rules has re- alistic meaning under the circumstance of the dynamic data changing.A new lnethod for the mining of dynalnic frequent itelnsets is prensented.This lnethod is developed based on previous episodes mining results.It only needs to scan part of the whole data set based on the previous resuhs for the whole frequent itemsets mining at the end,and experimental results show that the performance of this algorithm is outperform the Apriori algorithm.