MINING DENSE DATA: ASSOCIATION RULE DISCOVERY ON BENCHMARK CASE STUDY

Authors

  • Wan Aezwani Wan Abu Bakar School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu, Malaysia
  • Md. Yazid Md. Saman School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu, Malaysia
  • Zailani Abdullah School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu, Malaysia
  • Masita@Masila Abd Jalil School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu, Malaysia
  • Tutut Herawan Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.11113/jt.v77.6940

Keywords:

Data Mining (DM), Association Rule Mining (ARM), Rapid Miner (RM), frequent itemset, interestingness measure

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.

References

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Published

2015-12-21

Issue

Vol. 78 No. 2-2: Digital Media Issue

Section

Science and Engineering

License

Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

MINING DENSE DATA: ASSOCIATION RULE DISCOVERY ON BENCHMARK CASE STUDY. (2015). Jurnal Teknologi (Sciences & Engineering), 78(2-2). https://doi.org/10.11113/jt.v77.6940