EFP-M2: Efficient model for mining frequent patterns in transactional database
Discovering frequent patterns plays an essential role in many data mining applications. The aim of frequent patterns is to obtain the information about the most common patterns that appeared together. However, designing an efficient model to mine these patterns is still demanding due to the capacity...
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Springer Nature Switzerland
2012
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Online Access: | http://umpir.ump.edu.my/id/eprint/26642/ http://umpir.ump.edu.my/id/eprint/26642/ http://umpir.ump.edu.my/id/eprint/26642/1/EFP-M2-%20Efficient%20model%20for%20mining%20frequent%20patterns.pdf |
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ump-266422020-02-12T06:41:51Z http://umpir.ump.edu.my/id/eprint/26642/ EFP-M2: Efficient model for mining frequent patterns in transactional database Herawan, Tutut Noraziah, Ahmad Zailani, Abdullah Mustafa, Mat Deris H. Abawajyd, Jemal QA76 Computer software Discovering frequent patterns plays an essential role in many data mining applications. The aim of frequent patterns is to obtain the information about the most common patterns that appeared together. However, designing an efficient model to mine these patterns is still demanding due to the capacity of current database size. Therefore, we propose an Efficient Frequent Pattern Mining Model (EFP-M2) to mine the frequent patterns in timely manner. The result shows that the algorithm in EFP-M2l is outperformed at least at 2 orders of magnitudes against the benchmarked FP-Growth. Springer Nature Switzerland 2012 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26642/1/EFP-M2-%20Efficient%20model%20for%20mining%20frequent%20patterns.pdf Herawan, Tutut and Noraziah, Ahmad and Zailani, Abdullah and Mustafa, Mat Deris and H. Abawajyd, Jemal (2012) EFP-M2: Efficient model for mining frequent patterns in transactional database. In: 4th International Conference on Computational Collective Intelligence (ICCCI 2012), 28-30 November 2012 , Ho Chi Minh City, Viet Nam. pp. 29-38., 7654. ISSN 03029743 ISBN 978-364234706-1 https://doi.org/10.1007/978-3-642-34707-8_4 |
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QA76 Computer software Herawan, Tutut Noraziah, Ahmad Zailani, Abdullah Mustafa, Mat Deris H. Abawajyd, Jemal EFP-M2: Efficient model for mining frequent patterns in transactional database |
description |
Discovering frequent patterns plays an essential role in many data mining applications. The aim of frequent patterns is to obtain the information about the most common patterns that appeared together. However, designing an efficient model to mine these patterns is still demanding due to the capacity of current database size. Therefore, we propose an Efficient Frequent Pattern Mining Model (EFP-M2) to mine the frequent patterns in timely manner. The result shows that the algorithm in EFP-M2l is outperformed at least at 2 orders of magnitudes against the benchmarked FP-Growth. |
format |
Conference or Workshop Item |
author |
Herawan, Tutut Noraziah, Ahmad Zailani, Abdullah Mustafa, Mat Deris H. Abawajyd, Jemal |
author_facet |
Herawan, Tutut Noraziah, Ahmad Zailani, Abdullah Mustafa, Mat Deris H. Abawajyd, Jemal |
author_sort |
Herawan, Tutut |
title |
EFP-M2: Efficient model for mining frequent patterns in transactional database |
title_short |
EFP-M2: Efficient model for mining frequent patterns in transactional database |
title_full |
EFP-M2: Efficient model for mining frequent patterns in transactional database |
title_fullStr |
EFP-M2: Efficient model for mining frequent patterns in transactional database |
title_full_unstemmed |
EFP-M2: Efficient model for mining frequent patterns in transactional database |
title_sort |
efp-m2: efficient model for mining frequent patterns in transactional database |
publisher |
Springer Nature Switzerland |
publishDate |
2012 |
url |
http://umpir.ump.edu.my/id/eprint/26642/ http://umpir.ump.edu.my/id/eprint/26642/ http://umpir.ump.edu.my/id/eprint/26642/1/EFP-M2-%20Efficient%20model%20for%20mining%20frequent%20patterns.pdf |
first_indexed |
2023-09-18T22:41:36Z |
last_indexed |
2023-09-18T22:41:36Z |
_version_ |
1777416951563288576 |