Mining Association Rules: A Case Study on Benchmark Dense Data

Mustafa Bin Man (Universiti Malaysia Terengganu)
Wan Aezwani Wan Abu Bakar (Universiti Malaysia Terengganu)
Zailani Abdullah (Universiti Malaysia Kelantan)
Masita@Masila Abd Jalil (Universiti Malaysia Terengganu)
Tutut Herawan (University of Malaya)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 3, No 3: September 2016

Data mining is the process of discovering knowledge and previously unknown pattern from large amount of data. The association rule mining (ARM) has been in trend where a new pattern analysis can be discovered to project for an important prediction about any issues. Since the first introduction of frequent itemset mining, it has received a major attention among researchers and various efficient and sophisticated algorithms have been proposed to do frequent itemset mining. Among the best-known algorithms are Apriori and FP-Growth. In this paper, we explore these algorithms and comparing their results in generating association rules based on benchmark dense datasets. The datasets are taken from frequent itemset mining data repository. The two algorithms are implemented in Rapid Miner 5.3.007 and the performance results are shown as comparison. FP-Growth is found to be better algorithm when encountering the support-confidence framework.