计算机工程与应用Issue(22)：145-149,5.DOI:10.3778/j.issn.1002-8331.1312-0070
滑动窗口中数据流最大频繁项集挖掘算法研究
Mining algorithm research of data stream maximum frequent itemsets in sliding window
尹绍宏 1单坤玉 1范桂丹1
作者信息
- 1. 天津工业大学 计算机科学与软件学院，天津 300387
- 折叠
摘要
Abstract
The number of itemsets in data stream maximum frequent itemsets is relatively few and has implied all frequent itemsets, so mining data stream maximum frequent itemsets has better efficiency in time and space and has great significance. It has gotten more attention by the industry. In view of the data stream maximum frequent itemsets, this paper proposes a mining method called SWM-MFI based on matrix of data stream maximum frequent itemsets in sliding window. The method stores the data information using two Matrixes:one called transaction matrix stores the transaction data and the other one called 2-itemsets matrix stores frequent 2-itemsets. Frequent k-itemsets can be got through the 2-itemsets matrix and the maximum frequent itemsets can be mined based on the method of SWM-MFI. The theories and experiments show that the method is better on time efficiency.关键词
数据流/滑动窗口/最大频繁项集/矩阵Key words
data stream/sliding window/maximum frequent itemsets/matrix分类
信息技术与安全科学引用本文复制引用
尹绍宏,单坤玉,范桂丹..滑动窗口中数据流最大频繁项集挖掘算法研究[J].计算机工程与应用,2015,(22):145-149,5.
