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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