Mining Accurate Top-K Frequent Closed Itemset from Data Streams

摘要：

Frequent Closed Itemset 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 itemsets 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 the algorithm in this paper 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: Cao Xiaojun

作者单位: Information Engineering School Lanzhou University of Finance and Economic Lanzhou,China,730020

会议类型: 国际会议

会议名称: 2012 International Conference on Computer Science and Electronic Engineering(2012 IEEE计算机科学与电子工程国际会议 ICCSEE 2012)

会议地点: 杭州

会议语种:英文

页码: 180-184

在线出版日期: 2012-03-23（万方平台首次上网日期，不代表论文的发表时间）