Mining dense data: Association rule discovery on benchmark case study

Bakar, W.A.W.A. and Saman, M.D.M. and Abdullah, Z. and Jalil, M.A. and Herawan, T. (2016) Mining dense data: Association rule discovery on benchmark case study. Jurnal Teknologi, 78 (2-2). pp. 131-135. ISSN 0127-9696,

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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.

Item Type: Article
Uncontrolled Keywords: Data Mining (DM); Association Rule Mining (ARM); Rapid Miner (RM); Frequent itemset; Interestingness measure
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 06 Oct 2017 06:48
Last Modified: 06 Oct 2017 06:48

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