计算机应用研究2011，Vol.28Issue(7)：2519-2521,3.DOI:10.3969/j.issn.1001-3695.2011.07.033
数据流中一种基于滑动窗口的前K个频繁项集挖掘算法
Alogrithm for mining top-K frequent itemsets over sliding window in data streams
张文煜 1周满元1
作者信息
- 1. 桂林电子科技大学计算机科学与工程学院,广西桂林541004
- 折叠
摘要
Abstract
Frequent itemset mining over data streams is a hot topic in data mining and knowledge discovery. Hie features of data streams ,such as consecution, unboundedness and real-time,raise requirements for higher time and space performance of mining algorithms. The storage structure of the present algorithms need to be revised continually. Otherwise,the accuracy and rate of the present algorithms are low. The above two reasons lead up to disadvantage impact on both time and space efficiency. This paper designed a frequent itemset minming algorithm based on algonithm ECLAT and granular computing. It adopted the binary system to mean the item. Carried out displacement operation to update binary data. Meanwhile,used AND-operation for figuring up support threshold of itemset. The frequent itemset was put in OLL (orderd itemset list) by dichotomy,and output the top-K itemsets of the OLL. At last,experiments are performed to evaluate the excellent time and space efficiency of the al-gorithm.关键词
数据挖掘/数据流/频繁项集/滑动窗口/二进制/二分法Key words
data mining/ data stream/ frequent itemset/ sliding window/ binary/ dichotomy分类
信息技术与安全科学引用本文复制引用
张文煜,周满元..数据流中一种基于滑动窗口的前K个频繁项集挖掘算法[J].计算机应用研究,2011,28(7):2519-2521,3.
