基于频繁项集筛选策略的关联规则挖掘算法
A Novel Association Rules Mining Algorithm Based on Frequent Itemsets Filtering Strategy
在FP增长算法(FP-growth)和划分(Partition)算法的基础上提出了一种基于频繁项集筛选策略的关联规则挖掘的改进算法。提高了FP-growth算法的可伸缩性，结合划分算法思想将事务数据库进行合理划分在划分所得的各个局部数据集合上运用FP-growth算法，以减少算法的时空开销，实验结果证明了方法的有效性。
计算技术、计算机技术
侯选项集频繁项集频繁项集筛选筛选因子
佘名高,陈礼军.基于频繁项集筛选策略的关联规则挖掘算法[EB/OL].(2008-10-07)[2025-10-25].http://www.paper.edu.cn/releasepaper/content/200810-78.点此复制
Based on FP-Grouth and Parition ,we present a novel Association rules Mining Algorithm Based on Frequent Itemsets Filtering Strategy.In order to improving the scalalblity of FP-Growth ,we partition the DataBase into some local Datasets ,put the FP-Growth on each local DataSet and reduce the usage of cpu time and space.
Candidate itemsetsFrequent itemsetsFrequent itemsets filteringFiltering factor
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