计算机应用研究2012，Vol.29Issue(3)：837-840,4.DOI:10.3969/j.issn.1001-3695.2012.03.009
基于向量的数据流滑动窗口中最大频繁项集挖掘
Algorithm based on vector for mining maximal frequent itemsets in sliding window over data streams
摘要
Abstract
This paper proposed an algorithm based on vector for mining maximal frequent itemsets in sliding window over data streams (MFISW) aimed at the mining problems of maximal frequent itemsets over data streams. Firstly,the algorithm used vector to express items in data streams and solved the problem of time granularity through quantitative updating strategies. Secondly , it stored the ancillary information using a matrice and a array in creating the frequent sets through the bit operation, and improved the mining efficiency again using pruning technology during creating the maximal frequent sets. Finally, it improved the detecting efficiency by using a index list to store mining results. Theoretical analysis and experimental results show the algorithm is efficient.关键词
数据流/最大频繁项集/滑动窗口/向量Key words
data stream/ maximal frequent itemsets/ sliding window/ vector分类
信息技术与安全科学引用本文复制引用
徐嘉莉,陈佳,胡庆,黄波,郭红霞..基于向量的数据流滑动窗口中最大频繁项集挖掘[J].计算机应用研究,2012,29(3):837-840,4.基金项目
国家"863"计划资助项目(2007AA01Z443) （2007AA01Z443）
成都大学校基金资助项目(2010XJZ16) （2010XJZ16）
