一种混合型的增量数据关联规则挖掘算法

在数据库中增加数据且调整最小支持度时,数据库中关联规则会发生变化,为从数据量和最小支持度同时发生变化的数据库中快速获取频繁项集,发现变化后的关联规则,通过对FIM和AIUA算法进行分析,提出一种结合两种算法优点的增量数据关联规则挖掘My_FIM_AIUA算法,该算法能减少数据库扫描次数,减少候选项集数量。通过实验表明My_FIM_AIUA算法能在数据量和最小支持度同时变化时快速找到频繁项集,提高挖掘增量数据关联规则的速度。

A Hybrid Mining Algorithm for Incremental Data Association Rule

There will be some changes of association rules when adding data and adjusting the minimum support in the database. In order to obtain the frequent item sets quickly from the database when changes of the data size and minimum support happened at the same time, and to find out the changed association rule, the My_FIM_AIUA mining algorithm for incremental data association rule that combined the advantages of FIM and AIUA will be proposed by means of the analysis of FIM and AIUA algorithm. This algorithm can reduce the times of database scanning and decrease the numbers of candidate items. Thus, an experiment will be taken to show that the My_FIM_AIUA algorithm can search the frequent item sets quickly when changes of data size and minimum support happened at the same time, and it can improve the speed of mining the incremental data association rule.