ABDERRAHIM Boukhalat
بوخلط عبد الرحيم
abderrahim.boukhalat@univ-msila.dz
0550039220
- Informatics Department
- Faculty of Mathematics and Informatics
- Grade PHd
About Me
Master en informatique Option SIGL. in Université de M'sila
DomainMathématiques et Informatique
Research Domains
Parallel Computing Data Mining Artificial Intelligence
FiliereInformatique
Location
Msila, Msila
Msila, ALGERIA
Code RFIDE- 16-06-2018
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Master en informatique Option SIGL
Le développement d'un système d'information géographique pour la gestion d'un réseau électrique du SONELGAZ - 18-06-2016
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Licence en Informatique Option Système informatique
Atelier N5 : Création d'une plateforme E-commerce - 11-06-2007
- 1984-10-19 00:00:00
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ABDERRAHIM Boukhalat birthday
- 2024-12-10
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2024-12-10
Parallel Association Rules Mining Using GPUs and Reptile Search Algorithm
This paper proposes a novel approach to accelerate association rule mining using the Reptile Search Algorithm (RSA) in conjunction with GPU-based parallel processing. Traditional association rule mining techniques can be computationally expensive, especially with large datasets. By utilizing the inherent parallelism of Graphics Processing Units (GPUs), we significantly speed up the fitness evaluation process, a core component of the Reptile Search Algorithm. Our results show a marked improvement in the performance of RSA on large datasets, making it feasible for real-time or large scale applications such as market basket analysis, healthcare for drug interaction analysis, and web usage mining. We also analyze the impact of various GPU optimizations and present a comparison with CPU-based execution.
Citation
Abderrahim Boukhalat , Mohamed Benouis , BRAHIM Bouderah , Samir Akhrouf , kameleddine.heraguemi@univ-msila.dz, ,(2024-12-10), Parallel Association Rules Mining Using GPUs and Reptile Search Algorithm,The Sixth International Symposium on Informatics and Its Applications (ISIA),Université Mohamed Boudiaf M'Sila
- 2024-06-01
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2024-06-01
Reptile Search Algorithm for Association Rule Mining
Association rule mining (ARM) is a very popular, engaging, and active research area in data mining. It seeks to find valuable connections between different attributes in a defined dataset. ARM, which describes it as an NP-complete problem, creates a fertile field for optimization applications. The Reptile Search Algorithm (RSA) is an innovative evolutionary algorithm. It yanks stimulation from the encircling and hunting conducts of crocodiles. It is a well-known optimization technique for solving NP-complete issues. Since its introduction by Abualigah et al. in 2022, the approach has attracted considerable attention from researchers and has extensively been used to address diverse optimization issues in several disciplines. This is due to its satisfactory execution speed, efficient convergence rate, and superior effectiveness compared to other widely recognized optimization methods. This paper suggests a new version of the reptile search algorithm for resolving the association rules mining challenge. Our proposal inherits the trade-off between local and global search optimization issues demonstrated by the Reptile search algorithm. To illustrate the power of our proposal, a sequence of experiments is taken out on a varied, well-known, employing multiple comparison criteria. The results show an evident dominance of the proposed approach in the front of the famous association rules mining algorithms as well as Bees Swarm Optimization (BSO), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), and others regarding CPU time, fitness criteria, and the quality of generated rules.
Citation
Abderrahim Boukhalat , , (2024-06-01), Reptile Search Algorithm for Association Rule Mining, IJCDS Bahrain, Vol:15, Issue:1, pages:15, International Journal of Computing and Digital Systems
- 2024-06-01
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2024-06-01
Reptile Search Algorithm for Association Rule Mining
Association rule mining (ARM) is a very popular, engaging, and active research area in data mining. It seeks to find valuable connections between different attributes in a defined dataset. ARM, which describes it as an NP-complete problem, creates a fertile field for optimization applications. The Reptile Search Algorithm (RSA) is an innovative evolutionary algorithm. It yanks stimulation from the encircling and hunting conducts of crocodiles. It is a well-known optimization technique for solving NP-complete issues. Since its introduction by Abualigah et al. in 2022, the approach has attracted considerable attention from researchers and has extensively been used to address diverse optimization issues in several disciplines. This is due to its satisfactory execution speed, efficient convergence rate, and superior effectiveness compared to other widely recognized optimization methods. This paper suggests a new version of the reptile search algorithm for resolving the association rules mining challenge. Our proposal inherits the trade-off between local and global search optimization issues demonstrated by the Reptile search algorithm. To illustrate the power of our proposal, a sequence of experiments is taken out on a varied, well-known, employing multiple comparison criteria. The results show an evident dominance of the proposed approach in the front of the famous association rules mining algorithms as well as Bees Swarm Optimization (BSO), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), and others regarding CPU time, fitness criteria, and the quality of generated rules.
Citation
Abderrahim Boukhalat , Mohamed Benouis , BRAHIM Bouderah , Samir Akhrouf , kameleddine.heraguemi@univ-msila.dz, , (2024-06-01), Reptile Search Algorithm for Association Rule Mining, International Journal of Computing and Digital Systems, Vol:14, Issue:1, pages:16, University of Bahrain
- 2022
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2022
A Survey on Using Evolutionary Approaches-Based High-Utility Itemsets Mining
Frequently item-sets mining, also known as FIM, is a data mining technique used to extract useful knowledge from datasets. FIM is plagued by a number of issues, including high storage charges, a huge time and memory consumption. Classical FIM algorithms suffer with the great number of generated itemsets which contains useless items. And presupposes that all itemsets have the same importance. To deal with these limitations, high-utility itemsets (HUI) is proposed, which are as subset of FIM with the consideration of Utility or profit measure. In the last decade, evolutionary approaches become a trend to solve HUIs mining problem (EA). This paper explores the use of evolutionary techniques in HUIs mining. Moreover, we present an evolutionary techniques-based HUIs mining classification including single objective, multi-objective and Hybrid optimization techniques. This article provides a comparative …
Citation
Abderrahim Boukhalat , ,(2022), A Survey on Using Evolutionary Approaches-Based High-Utility Itemsets Mining,AID'2022,USTHB-ALGER