Mining accurate top-K frequent closed itemset from data stream

doi:10.1109/ICCSEE.2012.263

	Mining accurate top-K frequent closed itemset from data stream
	Cao, Xiaojun
	2012
会议名称	2012 International Conference on Computer Science and Electronics Engineering, ICCSEE 2012
会议录名称	Proceedings - 2012 International Conference on Computer Science and Electronics Engineering, ICCSEE 2012
卷号	2
页码	180-184
会议日期	2012-03-23 - 2012-03-25
会议地点	Hangzhou, Zhejiang, China
出版者	IEEE Computer Society
摘要	Frequent Closed Item set mining on data streams is of great significance. Though a minimum support threshold is assumed to be available in classical mining, it is hard to determine it in data streams. Hence, it is more reasonable to ask users to set a bound on the result size. Therefore, a real-time single-pass algorithm, called Top-k frequent closed item sets and a new way of updating the minimum support were proposed for mining top-K closed item sets from data streams efficiently. A novel algorithm, called Can(T), is developed for mining the essential candidate of closed item sets generated so far. Experimental results show that the proposed the algorithm in this paper is an efficient method for mining top-K frequent item sets from data streams. © 2012 IEEE.
关键词	Computer science Computers Electronics industry Engineering Industrial engineering Closed frequent itemsets Data stream Frequent closed itemsets Frequent item sets Minimum support Minimum support thresholds Novel algorithm Single-pass algorithm
DOI	10.1109/ICCSEE.2012.263
收录类别	EI
语种	英语
EI入藏号	20122115034395
文献类型	会议论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/9800
专题	信息工程与人工智能学院
作者单位	Information Engineering School, Lanzhou University of Finance and Economic, Lanzhou, 730020, China
推荐引用方式 GB/T 7714	Cao, Xiaojun. Mining accurate top-K frequent closed itemset from data stream[C]:IEEE Computer Society,2012:180-184.