Data Clustering using Two-Stage Eagle Strategy Based on Slime Mould Algorithm
(2022) In Journal of Computer Science 18(11). p.1062-1084- Abstract
Data clustering is considered an important component of data mining which aims to split a given dataset into disjoint groups having the same similarities. The developed techniques for clustering have some challenges to cluster entities in complex search space and most of them aim to maximize the sum of inter-cluster distances and minimize the sum of intra-cluster distances. This objective function is nonlinear and hard to optimize especially for complex search space. Metaheuristics are becoming a trend for solving this task thanks to their promising results. In this study, the eagle strategy is used to take advantage of the exploration provided by Levy Flight (LF) and the exploitation strength of the Slime Mould Algorithm (SMA) to solve... (More)
Data clustering is considered an important component of data mining which aims to split a given dataset into disjoint groups having the same similarities. The developed techniques for clustering have some challenges to cluster entities in complex search space and most of them aim to maximize the sum of inter-cluster distances and minimize the sum of intra-cluster distances. This objective function is nonlinear and hard to optimize especially for complex search space. Metaheuristics are becoming a trend for solving this task thanks to their promising results. In this study, the eagle strategy is used to take advantage of the exploration provided by Levy Flight (LF) and the exploitation strength of the Slime Mould Algorithm (SMA) to solve the clustering problem. The SMA algorithm is an efficient technique for solving complex optimization problems which has a high exploitation competence. On the other hand, LF tends to have good exploratory behavior. Our strategy exploits these advantages in a balanced way and through well-designed rounds to ensure the optimality of the clustering solutions. The proposed method is computationally efficient and inexpensive. It also achieves high accuracy in terms of average, worst, best, and the sum of intra-cluster distance. The method is also evaluated according to the speed of convergence and using statistical tests, namely Wilcoxon. The obtained results are compared with seven benchmarked metaheuristics, namely Grey Wolf Optimizer (GWO), Slime Mould Algorithm (SMA), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO) and Genetic Algorithm (GA) using eighteen datasets of shapes and UCI repositories.
(Less)
- author
- Oucheikh, Rachid LU ; Touil, Achraf and Fri, Mouhsene
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Clustering Evaluation, Data Clustering, Eagle Strategy, Levy Flight, Metaheuristic, Slime Mould Algorithm
- in
- Journal of Computer Science
- volume
- 18
- issue
- 11
- pages
- 23 pages
- publisher
- Science Publications
- external identifiers
-
- scopus:85164589851
- ISSN
- 1549-3636
- DOI
- 10.3844/jcssp.2022.1062.1084
- language
- English
- LU publication?
- yes
- id
- bb251c32-901c-4eb4-a2ee-fe6be057450e
- date added to LUP
- 2023-10-17 15:27:54
- date last changed
- 2023-10-17 15:27:54
@article{bb251c32-901c-4eb4-a2ee-fe6be057450e,
abstract = {{<p>Data clustering is considered an important component of data mining which aims to split a given dataset into disjoint groups having the same similarities. The developed techniques for clustering have some challenges to cluster entities in complex search space and most of them aim to maximize the sum of inter-cluster distances and minimize the sum of intra-cluster distances. This objective function is nonlinear and hard to optimize especially for complex search space. Metaheuristics are becoming a trend for solving this task thanks to their promising results. In this study, the eagle strategy is used to take advantage of the exploration provided by Levy Flight (LF) and the exploitation strength of the Slime Mould Algorithm (SMA) to solve the clustering problem. The SMA algorithm is an efficient technique for solving complex optimization problems which has a high exploitation competence. On the other hand, LF tends to have good exploratory behavior. Our strategy exploits these advantages in a balanced way and through well-designed rounds to ensure the optimality of the clustering solutions. The proposed method is computationally efficient and inexpensive. It also achieves high accuracy in terms of average, worst, best, and the sum of intra-cluster distance. The method is also evaluated according to the speed of convergence and using statistical tests, namely Wilcoxon. The obtained results are compared with seven benchmarked metaheuristics, namely Grey Wolf Optimizer (GWO), Slime Mould Algorithm (SMA), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO) and Genetic Algorithm (GA) using eighteen datasets of shapes and UCI repositories.</p>}},
author = {{Oucheikh, Rachid and Touil, Achraf and Fri, Mouhsene}},
issn = {{1549-3636}},
keywords = {{Clustering Evaluation; Data Clustering; Eagle Strategy; Levy Flight; Metaheuristic; Slime Mould Algorithm}},
language = {{eng}},
number = {{11}},
pages = {{1062--1084}},
publisher = {{Science Publications}},
series = {{Journal of Computer Science}},
title = {{Data Clustering using Two-Stage Eagle Strategy Based on Slime Mould Algorithm}},
url = {{http://dx.doi.org/10.3844/jcssp.2022.1062.1084}},
doi = {{10.3844/jcssp.2022.1062.1084}},
volume = {{18}},
year = {{2022}},
}