基于维间扩展和事务压缩的关联规则算法改进

Improvement of associative rule algorithm based on dimensional expansion and transaction reduction

Apriori是关联规则挖掘的经典算法,在利用该算法进行医疗数据挖掘的过程中,发现其频繁项集产生过程有一些不必要的开销,为此提出了改进算法Mypriori,利用维间扩展和事务压缩策略来提高频集发现的效率,并通过实验验证了算法的有效性。

Apriori algorithm is a classic algorithm for associative rules. When applying to medical data mining, Apriori will generate some unnecessary operations in the process of finding frequent item sets. An improved algorithm named Mypriori is presented. Mypriori uses two tactics to enhance frequent item sets producing, which are called dimensional expansion and transaction reduction. Mypriori is proved to be effective through data mining experiments.