Abstract. A statistical technique is developed for estimating the support of itemsets on data streams, regardless of the size of the data stored. This technique, which is computati...
Pierre-Alain Laur, Jean-Emile Symphor, Richard Noc...
Mining frequent patterns in a data stream is very challenging for the high complexity of managing patterns with bounded memory against the unbounded data. While many approaches as...
In this paper, we devise a method for the estimation of the true support of itemsets on data streams, with the objective to maximize one chosen criterion among {precision, recall}...
Pierre-Alain Laur, Richard Nock, Jean-Emile Sympho...
Mining of frequent closed itemsets has been shown to be more efficient than mining frequent itemsets for generating non-redundant association rules. The task is challenging in data...
Abstract. This paper introduces a new algorithm for approximate mining of frequent patterns from streams of transactions using a limited amount of memory. The proposed algorithm co...