基于分区分类法快速更新频繁项集

目前已提出了许多频繁项集更新算法,但是它们往往需要至少扫描一次原数据库,且会丢失一些重要规则。为此,文章提出了一种新的快速更新频繁项集算法CUFIA(Classifying Update Frequent Itemsets Algorithm),该算法通过对新增事务数据分区后快速逐一扫描,获得频繁项集,并将它们归入3个不同的类别,从而不需要扫描原数据库,便可有效地挖掘出其中的频繁项集,且不丢失重要规则。研究表明,该算法具有很好的可测量性。

Fast updating frequent itemsets based on classifying incremental transactions

Incremental association rules mining is an important content of data mining technology.This study proposes a new algorithm,called the Classifying Update Frequent Itemsets Algorithm(CUFIA) for efficiently incrementally mining association rules from large transaction database.Rather than rescanning the original database for some new generated frequent itemsets,CUFIA partitions the incremental database logically according to unit time interval,then accumulates the occurrence counts of new generated frequent itemsets and deletes infrequent itemsets obviously by backward method.Besides,it divides these frequent itemsets into three categories.Thus CUFIA can discover new frequent itemsets more efficiently and need rescan the original database only once to get overall frequent itemsets in the final database if necessary.CUFIA has good scalability in our simulation.