Mining Accurate Top-K Frequent Closed Itemset from Data Streams
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.
data streams closed frequent itemsets top-K
Cao Xiaojun
Information Engineering School Lanzhou University of Finance and Economic Lanzhou,China,730020
国际会议
杭州
英文
180-184
2012-03-23(万方平台首次上网日期,不代表论文的发表时间)