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...
The paper presents a method for pruning frequent itemsets based on background knowledge represented by a Bayesian network. The interestingness of an itemset is defined as the abso...
A complete set of frequent itemsets can get undesirably large due to redundancy when the minimum support threshold is low or when the database is dense. Several concise representat...
In this paper we propose a new and elegant approach toward the generalization of frequent itemset mining to the multirelational case. We define relational itemsets that contain i...
We describe here a general approach for rare itemset mining. While mining literature has been almost exclusively focused on frequent itemsets, in many practical situations rare on...