基于数据挖掘的Apriori算法研究与改进

Research and Improvement of Apriori Algorithm based on Data Mining

Apriori算法是一种基于挖掘布尔关联规则频繁项集的算法,很多挖掘算法都是在Apriori算法的基础上加以改进的。然而,该算法产生K-项频繁项集时需要对大型事务数据库扫描K次,多次扫描大型数据库将直接影响到算法的执行效率。本文通过对数据挖掘主要任务的研究,结合数据挖掘过程的需求和Apriori算法存在的问题,阐述了对Apriori算法的改进意见。表明改进的Apriori算法可以减少K-项集中CK的数量,进而提高数据库扫描效率。同时也说明了数据挖掘技术对计算机技术发展的推动作用。

Apriori algorithm is a kind of algorithm based on mining Boolean association rules for frequent item sets,and many mining algorithms are improved on the basis of it.However,it is required to scan large transaction databases for k times when this algorithm generates a K-item frequent item set,so the multi scan of large databases will directly affect the efficiency of the algorithm.Combined with the needs in data mining process and the problems of Apropri algorithm,this paper describes several improvement suggestions to the algorithm with research on main tasks of data mining.The result shows that the improved Apriori algorithm can reduce the number of CK in K-item sets and then increase the efficiency of database scanning.Meanwhile,this paper also elaborates that the data mining technology promotes the development of computer technology.