计算机工程与应用Issue(8)：110-113,132,5.DOI:10.3778/j.issn.1002-8331.1110-0046
对数据流频繁项集挖掘算法WSW-Imp的改进
摘要
Abstract
In recent years, with the emergence of new applications, such as network traffic analysis, on-line transaction analysis, and network intrusion detection, data mining has become an important research topic. To the question of mining frequent item-sets in data streams, most of researches are based on traditional window models, i.e.the titled-time window model, the landmark window model, and the sliding window model. A new time window model named the weighted sliding window model is pro-posed by Pauray S.M.Tsai in 2009. In the same paper the author also proposed two algorithms, called WSW and WSW-Imp, where WSW-Imp is to improve the efficiency of WSW, to mine frequent itemsets in data streams using this window model. In this paper, after studying the weighted sliding window model and the algorithm of WSW-Imp, it proposes an algorithm named WSW-Imp2 to improve WSW-Imp further. Moreover, it proves that the algorithm WSW-Imp2 is more effective than WSW-Imp. Empirical results also show the conclusion.关键词
数据挖掘/数据流/数据流挖掘/频繁项集/加权滑动窗口模式Key words
data mining/data streams/data streams mining/frequent itemsets/weighted sliding window model分类
信息技术与安全科学引用本文复制引用
王晓霞,王治和..对数据流频繁项集挖掘算法WSW-Imp的改进[J].计算机工程与应用,2013,(8):110-113,132,5.基金项目
国家自然科学基金地区科学基金项目(No.61163036) （No.61163036）
