Mining dense data: Association rule discovery on benchmark case study

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 compar...

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Main Authors:	Bakar, W.A.W.A., Saman, M.D.M., Abdullah, Z., Jalil, M.A., Herawan, T.
Format:	Article
Published:	Penerbit UTM Press 2016
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.um.edu.my/17907/
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Institution:	Universiti Malaya

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spelling	my.um.eprints.179072017-10-06T06:48:35Z http://eprints.um.edu.my/17907/ Mining dense data: Association rule discovery on benchmark case study Bakar, W.A.W.A. Saman, M.D.M. Abdullah, Z. Jalil, M.A. Herawan, T. QA75 Electronic computers. Computer science 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. Penerbit UTM Press 2016 Article PeerReviewed 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
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Bakar, W.A.W.A. Saman, M.D.M. Abdullah, Z. Jalil, M.A. Herawan, T. Mining dense data: Association rule discovery on benchmark case study
description	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.
format	Article
author	Bakar, W.A.W.A. Saman, M.D.M. Abdullah, Z. Jalil, M.A. Herawan, T.
author_facet	Bakar, W.A.W.A. Saman, M.D.M. Abdullah, Z. Jalil, M.A. Herawan, T.
author_sort	Bakar, W.A.W.A.
title	Mining dense data: Association rule discovery on benchmark case study
title_short	Mining dense data: Association rule discovery on benchmark case study
title_full	Mining dense data: Association rule discovery on benchmark case study
title_fullStr	Mining dense data: Association rule discovery on benchmark case study
title_full_unstemmed	Mining dense data: Association rule discovery on benchmark case study
title_sort	mining dense data: association rule discovery on benchmark case study
publisher	Penerbit UTM Press
publishDate	2016
url	http://eprints.um.edu.my/17907/
_version_	1643690553074778112

Mining dense data: Association rule discovery on benchmark case study

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