A modified multi-objective slime mould algorithm with orthogonal learning for numerical association rules mining

Association rule mining (ARM) is defined by its crucial role in finding common pattern in data mining. It has different types such as fuzzy, binary, numerical. In this paper, we introduce a multi-objective orthogonal mould algorithm (MOOSMA) with numerical association rule mining (NARM) which is a d...

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Vydáno v:	Neural computing & applications Ročník 35; číslo 8; s. 6125 - 6151
Hlavní autoři:	Yacoubi, Salma, Manita, Ghaith, Amdouni, Hamida, Mirjalili, Seyedali, Korbaa, Ouajdi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.03.2023 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Benchmarks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data mining Data Mining and Knowledge Discovery Image Processing and Computer Vision Machine learning Multiple objective analysis Optimization Original Article Probability and Statistics in Computer Science Source code Multi-objective optimization Swarm intelligence Orthogonal learning Association rules mining
ISSN:	0941-0643, 1433-3058
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Abstract	Association rule mining (ARM) is defined by its crucial role in finding common pattern in data mining. It has different types such as fuzzy, binary, numerical. In this paper, we introduce a multi-objective orthogonal mould algorithm (MOOSMA) with numerical association rule mining (NARM) which is a different type of ARM. Existing algorithms that deal with the NARM problem can be classified into three categories: distribution, discretization and optimization. The proposed approach belongs to the optimization category which is considered as a better way to deal with the problem. Our main objective is based on four efficiency measures related to each association: Support, Confidence, Comprehensibility, Interestingness. To test the performance of our approach, we started by testing our method on widely known generalized dynamic benchmark tests called CEC’09. This benchmark is composed of 20 test functions: 10 functions without constraints and 10 functions with constraints. Secondly, we applied our algorithm to solve NARM problem using 10 frequently used real-world datasets. Experimental analysis shows that our algorithm MOOSMA has better results in terms of Average Support, Average Confidence, Average Lift, Average Certain factor and Average Netconf. Note that source code of the MOOSMA algorithm is publicly available at https://github.com/gaithmanita/MOOSMA .
AbstractList	Association rule mining (ARM) is defined by its crucial role in finding common pattern in data mining. It has different types such as fuzzy, binary, numerical. In this paper, we introduce a multi-objective orthogonal mould algorithm (MOOSMA) with numerical association rule mining (NARM) which is a different type of ARM. Existing algorithms that deal with the NARM problem can be classified into three categories: distribution, discretization and optimization. The proposed approach belongs to the optimization category which is considered as a better way to deal with the problem. Our main objective is based on four efficiency measures related to each association: Support, Confidence, Comprehensibility, Interestingness. To test the performance of our approach, we started by testing our method on widely known generalized dynamic benchmark tests called CEC’09. This benchmark is composed of 20 test functions: 10 functions without constraints and 10 functions with constraints. Secondly, we applied our algorithm to solve NARM problem using 10 frequently used real-world datasets. Experimental analysis shows that our algorithm MOOSMA has better results in terms of Average Support, Average Confidence, Average Lift, Average Certain factor and Average Netconf. Note that source code of the MOOSMA algorithm is publicly available at https://github.com/gaithmanita/MOOSMA. Association rule mining (ARM) is defined by its crucial role in finding common pattern in data mining. It has different types such as fuzzy, binary, numerical. In this paper, we introduce a multi-objective orthogonal mould algorithm (MOOSMA) with numerical association rule mining (NARM) which is a different type of ARM. Existing algorithms that deal with the NARM problem can be classified into three categories: distribution, discretization and optimization. The proposed approach belongs to the optimization category which is considered as a better way to deal with the problem. Our main objective is based on four efficiency measures related to each association: Support, Confidence, Comprehensibility, Interestingness. To test the performance of our approach, we started by testing our method on widely known generalized dynamic benchmark tests called CEC’09. This benchmark is composed of 20 test functions: 10 functions without constraints and 10 functions with constraints. Secondly, we applied our algorithm to solve NARM problem using 10 frequently used real-world datasets. Experimental analysis shows that our algorithm MOOSMA has better results in terms of Average Support, Average Confidence, Average Lift, Average Certain factor and Average Netconf. Note that source code of the MOOSMA algorithm is publicly available at https://github.com/gaithmanita/MOOSMA .
Author	Manita, Ghaith Korbaa, Ouajdi Yacoubi, Salma Amdouni, Hamida Mirjalili, Seyedali
Author_xml	– sequence: 1 givenname: Salma surname: Yacoubi fullname: Yacoubi, Salma organization: Laboratory MARS, LR17ES05, ISITCom, University of Sousse – sequence: 2 givenname: Ghaith surname: Manita fullname: Manita, Ghaith email: gaith.manita@esen.tn organization: Laboratory MARS, LR17ES05, ISITCom, University of Sousse, ESEN, University of Manouba – sequence: 3 givenname: Hamida surname: Amdouni fullname: Amdouni, Hamida organization: ESEN, University of Manouba, Laboratory RIADI, ENSI, University of Manouba – sequence: 4 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali organization: Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, YFL (Yonsei Frontier Lab), Yonsei University – sequence: 5 givenname: Ouajdi surname: Korbaa fullname: Korbaa, Ouajdi organization: Laboratory MARS, LR17ES05, ISITCom, University of Sousse, ISITCom, University of Sousse
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Snippet	Association rule mining (ARM) is defined by its crucial role in finding common pattern in data mining. It has different types such as fuzzy, binary, numerical....
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SubjectTerms	Algorithms Artificial Intelligence Benchmarks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data mining Data Mining and Knowledge Discovery Image Processing and Computer Vision Machine learning Multiple objective analysis Optimization Original Article Probability and Statistics in Computer Science Source code
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Title	A modified multi-objective slime mould algorithm with orthogonal learning for numerical association rules mining
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