In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the c...
We present a generalization of frequent itemsets allowing the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifie...