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A semi-apriori algorithm for discovering the frequent itemsets

Mining the frequent itemsets are still one of the data mining research challenges. Frequent itemsets generation produce extremely large numbers of generated itemsets that make the algorithms inefficient. The reason is that the most traditional approaches adopt an iterative strategy to discover the i...

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Main Authors: Fageeri, S.O., Ahmad, R., Baharudin, B.B.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2014
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938765772&doi=10.1109%2fICCOINS.2014.6868358&partnerID=40&md5=43d9806c0645660332a405f83c3f4dc0
http://eprints.utp.edu.my/31244/
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spelling my.utp.eprints.312442022-03-25T09:03:57Z A semi-apriori algorithm for discovering the frequent itemsets Fageeri, S.O. Ahmad, R. Baharudin, B.B. Mining the frequent itemsets are still one of the data mining research challenges. Frequent itemsets generation produce extremely large numbers of generated itemsets that make the algorithms inefficient. The reason is that the most traditional approaches adopt an iterative strategy to discover the itemsets, that's require very large process. Furthermore, the present mining algorithms cannot perform efficiently due to high and repeatedly database scan. In this paper we introduce a new binary-based Semi-Apriori technique that efficiently discovers the frequent itemsets. Extensive experiments had been carried out using the new technique, compared to the existing Apriori algorithms, a tentative result reveal that our technique outperforms Apriori algorithm in terms of execution time. © 2014 IEEE. Institute of Electrical and Electronics Engineers Inc. 2014 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938765772&doi=10.1109%2fICCOINS.2014.6868358&partnerID=40&md5=43d9806c0645660332a405f83c3f4dc0 Fageeri, S.O. and Ahmad, R. and Baharudin, B.B. (2014) A semi-apriori algorithm for discovering the frequent itemsets. In: UNSPECIFIED. http://eprints.utp.edu.my/31244/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Mining the frequent itemsets are still one of the data mining research challenges. Frequent itemsets generation produce extremely large numbers of generated itemsets that make the algorithms inefficient. The reason is that the most traditional approaches adopt an iterative strategy to discover the itemsets, that's require very large process. Furthermore, the present mining algorithms cannot perform efficiently due to high and repeatedly database scan. In this paper we introduce a new binary-based Semi-Apriori technique that efficiently discovers the frequent itemsets. Extensive experiments had been carried out using the new technique, compared to the existing Apriori algorithms, a tentative result reveal that our technique outperforms Apriori algorithm in terms of execution time. © 2014 IEEE.
format Conference or Workshop Item
author Fageeri, S.O.
Ahmad, R.
Baharudin, B.B.
spellingShingle Fageeri, S.O.
Ahmad, R.
Baharudin, B.B.
A semi-apriori algorithm for discovering the frequent itemsets
author_facet Fageeri, S.O.
Ahmad, R.
Baharudin, B.B.
author_sort Fageeri, S.O.
title A semi-apriori algorithm for discovering the frequent itemsets
title_short A semi-apriori algorithm for discovering the frequent itemsets
title_full A semi-apriori algorithm for discovering the frequent itemsets
title_fullStr A semi-apriori algorithm for discovering the frequent itemsets
title_full_unstemmed A semi-apriori algorithm for discovering the frequent itemsets
title_sort semi-apriori algorithm for discovering the frequent itemsets
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938765772&doi=10.1109%2fICCOINS.2014.6868358&partnerID=40&md5=43d9806c0645660332a405f83c3f4dc0
http://eprints.utp.edu.my/31244/
_version_ 1738657220630937600
score 13.211869

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