Analysis of Frequent Itemsets Mining Algorithm Againts Models of Different Datasets

Abstract

Data mining is a study that uses statistical knowledge, mathematical calculations, artificial intelligence methods, machine learning by extracting and identifying useful information and related knowledge from various large databases. One of them is looking for itemsets combination from the data stack, the search process can be done using the Apriori Association Rules algorithm, the FPGrowth Association Rule and Closed Association rule. The three algorithms are some of the implementations of frequent itemsets search methods. Two datasets will be tested, namely retail datasets and accidents datasets derived from fimi datasets, with the aim of knowing the behavior of the three algorithms against the dataset model we tested. Each algorithm will test both datasets, with minimum support values for retail datasets ranging from 0.01-0.05 and min-conf 0.01-0.05. Likewise on the accident min-sup and min-conf 0.6-1 datasets. For retail algorithm data with the fastest processing time, the FP-Growth AR algorithm is more than 85% compared to Apriori-AR, followed by AR-Apriori and Apriori-Close-AR. For detailed memory usage results the Apriori-AR algorithm is lighter and outperforms. Accident data the experimental results show that FP-Growth-AR is a little more memory efficient and the fastest process is Apriori-Close, while the Apriori-AR with the longest processing time.