An analytical study on frequent itemset mining algorithms

Abstract

Data mining is the process of collecting, extracting and analyzing large data set from different perspectives. Fundamental and important task of data mining is the mining of frequent itemsets. Frequent itemsets play an important role in association rule mining. Many researchers invented ideas to generate the frequent itemsets. The execution time required for generating frequent itemsets play an important role. This study yields a detailed analysis of the FP-Growth, Eclat and SaM algorithms to illustrate the performance with standard datasets Hepatitis and Adault. The comparative study of FP-Growth, Eclat and SaM algorithms includes aspects like different support values and different datasets. © 2013 Springer International Publishing.