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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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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
score 13.18916

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