摘要
已有的滑动窗口数据流模型没有考虑过时数据和事务数量对挖掘结果的影响.针对该问题.提出了一种新的动态权值滑动窗口的数据流模型,并将该模型应用于数据流频繁项集挖掘中,设计了动态权值滑动窗口的频繁项集挖掘算法FIMDWS和改进算法FIMDWSW-Imp.通过实验对算法做了分析和评价.
In the existing literatures,sl iding window models does not consider the mining effect of outdated data and the num-ber of transactions. To solve this problem,this paper proposes a novel dynamic weighted sliding window data stream model. Firstly, we apply this model to the data stream frequent itemsets mining,and design and presents the algorithms of FIMDWSW（ Frequent Itemsets Mining in Dynamic Weighted Sliding Window） and imporved FIMDWSW - IMP （Frequent Itemsets Mining in Dynamic Weighted Sliding Window - Improvment） for dynamic weighted sliding window data stream model. The performance of algorithms are analyzed and evaluated in numerical experiments.
作者
白川平
杨志翀
BAI Chuanping;YANG Zhichong(School of Mathematics and Computer Science,Ningxia Normal University, Guyuan Ningxia 756000;School of Electronic and Information Engineering,Lanzhou Jiaotong University, Lanzhou Gansu 730070)
出处
《宁夏师范学院学报》
2017年第6期49-55,共7页
Journal of Ningxia Normal University
基金
宁夏自然科学基金资助项目(NZ15264)
甘肃省教育厅高等学校科研经费项目(2017A-027)
甘肃省自然科学基金项目(1506RJZA072)
关键词
数据流
频繁项集
权值滑动窗口
Data stream
Frequent item sets
Weighted sliding window
