Comparison of Apriori, FP-growth and Dynamic FP- growth for Frequent Patterns

Abstract

Frequent pattern mining is one of the active research themes in data mining. It is an important role in all data mining tasks such as clustering, classification, prediction and association analysis. Frequent pattern is the most time consuming process due to a massive number of patterns generated. Frequent patterns are generated by using association rule mining algorithms that use candidate generation and association rules such as Apriori algorithm, and the algorithms without candidate set generation and FP-tree such as FP-growth and DynFP-growth algorithms. In this paper, this system used computer sales items for generating frequent patterns by applying Apriori, FP-Growth and DynFP-Growth algorithms. The frequent patterns are used for comparing performance results with run time and scalability. The scalability and run time of DynFP-Growth algorithm is faster than Apriori and FP-Growth algorithms.