计算机应用研究2017，Vol.34Issue(11)：3379-3383,5.DOI:10.3969/j.issn.1001-3695.2017.11.039
减少候选项集的数据流高效用项集挖掘算法
High utility itemsets mining algorithm of data stream with reducing candidate itemsets
摘要
Abstract
In the big data stream scenario,high utility pattern mining algorithm generated a lot of candidate itemsets and reduced the efficiency of time and space of algorithm.This paper proposed a high utility itemsets mining algorithm of data stream with reducing candidate itemsets to resolve that problem.Firstly,it constructed a global tree through a single scan of the current window in a data stream,reduced redundancy utilities in both entries of a header table and nodes in the tree in this stage.Secondly,it generated candidate patterns from the constructed tree,reduced the redundancy utilities of local tree by growth algorithm.Lastly,it identified a set of high utility patterns from the candidate patterns.Realistic data streams based experimental results show that the proposed algorithm performs better in efficiency of time and space and memory usage index than the other high utility pattern mining algorithm of data streams.关键词
大数据/数据流/高效用项集/模式挖掘/模式增长/候选模式Key words
big data/data stream/high utility itemsets/pattern mining/pattern growth/candidate pattern分类
信息技术与安全科学引用本文复制引用
茹蓓,贺新征..减少候选项集的数据流高效用项集挖掘算法[J].计算机应用研究,2017,34(11):3379-3383,5.基金项目
河南省科技厅软科学研究计划资助项目(152400410345) （152400410345）
河南省教育厅资助项目(15A520093) （15A520093）
河南省科技厅科技攻关资助项目(172102210445) （172102210445）
