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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Vydané v:Neural computing & applications Ročník 35; číslo 8; s. 6125 - 6151
Hlavní autori: Yacoubi, Salma, Manita, Ghaith, Amdouni, Hamida, Mirjalili, Seyedali, Korbaa, Ouajdi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.03.2023
Springer Nature B.V
Predmet:
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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Shrnutí: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 .
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07985-w