We explore in this paper a practicably interesting mining task to retrieve frequent itemsets with memory constraint. As opposed to most previous works that concentrate on improvin...
As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is b...
Adam Kirsch, Michael Mitzenmacher, Andrea Pietraca...
In this work we study the mining of top-K frequent closed itemsets, a recently proposed variant of the classical problem of mining frequent closed itemsets where the support thresh...
Generalized association rule mining is an extension of traditional association rule mining to discover more informative rules, given a taxonomy. In this paper, we describe a forma...
Traditional methods for frequent itemset mining typically assume that data is centralized and static. Such methods impose excessive communication overhead when data is distributed...
Matthew Eric Otey, Chao Wang, Srinivasan Parthasar...