On the estimation of frequent itemsets for data streams
Pierre-Alain Laur, Richard Nock, Jean-Émile Symphor, Pascal Poncelet · 2005
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} while ensuring a degradation as reduced as possible for the other criterion. We discuss the strengths, weaknesses and range of applicability of this method that relies on conventional uniform convergence results, yet guarantees statistical optimality from different standpoints.