A novel approach for mining frequent itemsets: AprioriMin

Abstract

The step of mining frequent itemsets in database is the essential step and most expensive in the process of mining association rules in data mining task, many algorithms of mining frequent itemsets have been proposed to improve the performance of Apriori Algorithm. In this paper, we have introduced an optimization in the phase of generation pruning of candidates by a new strategy for the calculation of frequent itemsets based on approximate values of supports exact the itemsets. We have evaluated our algorithm AprioriMin against three popular frequent itemsets mining algorithms - Apriori and FP-growth, Close using two data sets with a variety of minimum support.

DOI: 10.1109/CIST.2016.7805057

Cite this paper

@article{Essalmi2016ANA, title={A novel approach for mining frequent itemsets: AprioriMin}, author={Houda Essalmi and Mohamed El Far and Mohammed El Mohajir and Mohamed Chahhou}, journal={2016 4th IEEE International Colloquium on Information Science and Technology (CiSt)}, year={2016}, pages={286-289} }