Estimating Itemsets of Interst by Sampling

Shichao Zhang, Chengqi Zhang

Abstract: Many applications such as marketing and stock investment may be time limited and few considerations to accurate itemsets. For these applications, we present a mining model to quickly estimate the approximate support of frequent itemsets of interest in large scale databases in this paper. An efficient algorithm is thus designed to reduce the searched space by pruning uninterested frequent itemsets.