The Comparison of Apriori Algorithm with Preprocessing and FP-Growth Algorithm for Finding Frequent Data Pattern in Association Rule
Deo Wicaksono, Muhammad Ihsan Jambak, Danny Matthew Saputra · 2020
Association Rules is a data mining method to find the relation between items called rules.Finding rules in the association method can be divided into two phases.The first phase is finding the frequent pattern which satisfies specified minimum frequent, and the second phase is finding strict rules from the frequent pattern which satisfy the minimum support and confidence.The main problem of Association Rules is based on the algorithm used, and this method takes a large amount of memory and time-consuming.This study aims to add preprocessing using the aggregate function on the Apriori Algorithm and therefore improve the memory and time consumption for finding a large number of rules.