Mining dense data: Association rule discovery on benchmark case study
Document Type
Article
Publication Date
1-1-2016
Abstract
Data Mining (DM), is the process of discovering knowledge and previously unknown pattern from large amount of data. The association rule mining has been in trend where a new pattern analysis can be discovered to project for an important prediction about any issues. In this article, we present comparison result between Apriori and FP-Growth algorithm in generating association rules based on a benchmark data from frequent itemset mining data repository. Experimentation with the two (2) algorithms are done in Rapid Miner 5.3.007 and the performance result is shown as a comparison. The results obtained confirmed and verified the results from the previous works done.
Keywords
Data Mining (DM), Association Rule Mining (ARM), Rapid Miner (RM), Frequent itemset, Interestingness measure
Divisions
fsktm
Publication Title
Jurnal Teknologi
Volume
78
Issue
2-2
Publisher
Penerbit UTM Press