Mining Top-K Frequent Closed Itemset in Data Streams

摘要：

Mining closed frequent itemsets from data streams is of interest recently. However, it is not easy for users to determine a proper minimum support threshold. Hence, it is more reasonable to ask users to set a bound on the result size. Therefore, a realtime single-pass algorithm, called Top-k-FCI and a new way of updating the minimum support were proposed for mining top-K closed itemsets from data streams efficiently. A novel algorithm, called can(T), is developed for mining the essential candidate of closed itemsets generated so far. Experimental results show that the proposed Top-k-FCI algorithm is an efficient method for mining top-K frequent itemsets from data streams.

关键词： data streams closed frequent itemsets top-K

作者: Jun Li Xiuhong Hou Sen Gong

作者单位: Network Information Center, Henan University Kaifeng, 475001 Henan, China Computing Center,Henan University Kaifeng, 475001 Henan, China Henan University Kaifeng, 475001 Henan, China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Automation Engineering(ICCAE 2011)(2011年第三届IEEE计算机与自动化工程国际会议)

会议地点: 重庆

会议语种:英文

页码: 75-79

在线出版日期: 2011-01-21（万方平台首次上网日期，不代表论文的发表时间）