Discovering association rules by identifying relationships among sets of items in a transaction database is an important problem in Data Mining. Finding frequent itemsets is compu...
We present two efficient Apriori implementations of Frequent Itemset Mining (FIM) that utilize new-generation graphics processing units (GPUs). Our implementations take advantage ...
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...
Computing frequent itemsets and maximally frequent itemsets in a database are classic problems in data mining. The resource requirements of all extant algorithms for both problems...
Ganesh Ramesh, William Maniatty, Mohammed Javeed Z...
Abstract. In this paper, we propose a novel mining task: mining frequent superset from the database of itemsets that is useful in bioinformatics, e-learning systems, jobshop schedu...