计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
8期
110-113
,共4页
数据挖掘%数据流%数据流挖掘%频繁项集%加权滑动窗口模式
數據挖掘%數據流%數據流挖掘%頻繁項集%加權滑動窗口模式
수거알굴%수거류%수거류알굴%빈번항집%가권활동창구모식
data mining%data streams%data streams mining%frequent itemsets%weighted sliding window model
近年来随着新的应用的出现,比如网络流量分析、在线事物分析和网络欺诈检测等,对数据流的挖掘成了一个越来越重要的课题.对于数据流频繁项集的挖掘,目前绝大部分的研究都集中在传统的窗口模式下进行,即时间衰退窗口模式、界标窗口模式和滑动窗口模式.Pauray S.M.Tsai于2009年提出了一种新的窗口模式:加权滑动窗口模式,并设计了两个基于此窗口模式的数据流频繁项集挖掘算法WSW和WSW-Imp,其中WSW-Imp是对WSW算法的改进.在研究了加权滑动窗口模式以及WSW-Imp算法的基础上,对WSW-Imp算法作了进一步的改进,设计了算法WSW-Imp2,并从理论上证明了WSW-Imp2算法比WSW-Imp算法更高效,实验结果也表明了这一点.
近年來隨著新的應用的齣現,比如網絡流量分析、在線事物分析和網絡欺詐檢測等,對數據流的挖掘成瞭一箇越來越重要的課題.對于數據流頻繁項集的挖掘,目前絕大部分的研究都集中在傳統的窗口模式下進行,即時間衰退窗口模式、界標窗口模式和滑動窗口模式.Pauray S.M.Tsai于2009年提齣瞭一種新的窗口模式:加權滑動窗口模式,併設計瞭兩箇基于此窗口模式的數據流頻繁項集挖掘算法WSW和WSW-Imp,其中WSW-Imp是對WSW算法的改進.在研究瞭加權滑動窗口模式以及WSW-Imp算法的基礎上,對WSW-Imp算法作瞭進一步的改進,設計瞭算法WSW-Imp2,併從理論上證明瞭WSW-Imp2算法比WSW-Imp算法更高效,實驗結果也錶明瞭這一點.
근년래수착신적응용적출현,비여망락류량분석、재선사물분석화망락기사검측등,대수거류적알굴성료일개월래월중요적과제.대우수거류빈번항집적알굴,목전절대부분적연구도집중재전통적창구모식하진행,즉시간쇠퇴창구모식、계표창구모식화활동창구모식.Pauray S.M.Tsai우2009년제출료일충신적창구모식:가권활동창구모식,병설계료량개기우차창구모식적수거류빈번항집알굴산법WSW화WSW-Imp,기중WSW-Imp시대WSW산법적개진.재연구료가권활동창구모식이급WSW-Imp산법적기출상,대WSW-Imp산법작료진일보적개진,설계료산법WSW-Imp2,병종이론상증명료WSW-Imp2산법비WSW-Imp산법경고효,실험결과야표명료저일점.
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.
