建立频繁项目集向量的极大频繁项目集挖掘

在分析和研究诸多经典关联规则挖掘算法或最大频繁项目集挖掘算法的基础上,提出了一种新的极大频繁项目集挖掘算法BOFPV_MMFIA算法.该算法引入频繁项目集向量FP-V,将极大频繁项目集的挖掘过程转化为频繁项目集向量FP-V的与运算过程.算法只需扫描数据库一次,克服了Apriori及其相关算法产生大量候选集和需多次扫描数据库的缺点.又不同于BOM算法,挖掘频繁k_项目集时,需要进行 次k个向量的与运算.因此,BOFPV_MMFIA算法的效率明显高于Apriori、DMFIA及BOM算法.

Maximal frequent item set mining with establishment of frequent item set vectors

After analyzing many typical association rule mining algorithms and the maximal frequent item set mining algorithm,a new algorithm of maximal frequent item set mining,named as BOFPV_MMFIA,is proposed.A frequent item set vector FP-V is introduced so as to convert the course of maximal frequent item set mining into the course of AND operation of the FP_V vector.The existing Apriori and its related algorithms produce a lot of candidacy sets and need scanning database many times,and the BOM algorithm entails the AND operation of k vectors with times.Overcoming these drawbacks,the proposed BOFPV_MFIA algorithm needs scanning database only once.Therefore,the proposed algorithm is obviously superior to Apriori and BOM algorithm in efficiency.