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...
| Main Authors: | Fageeri, S.O., Ahmad, R., Baharudin, B.B. |
|---|---|
| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.31244 / |
| 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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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 |
| collection |
UTP Institutional Repository |
| 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_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_ |
1741197541755584512 |
| score |
11.62408 |