Parameters Sensitivity Analysis of Ant Colony Based Clustering: Application for Student Grouping in Collaborative Learning Environment

Clustering analysis is one of the data analysis techniques that organizes items into clusters according to their degrees of similarities. In this context, bio-inspired algorithms have found success in solving clustering problems. Inspired by nature, Ant Colony based Clustering arises from ant colony...

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Bibliographic Details
Published in:IEEE access Vol. 12; pp. 24751 - 24761
Main Authors: Abid, Abir, Kallel, Ilhem, Sanchez-Medina, Javier J., Ayed, Mounir Ben
Format: Journal Article
Language:English
Published: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
alpha parameter
alpha similarity
Ant colony algorithms parameters
ant colony clustering (ACC)
Ant colony optimization
ant colony optimization (ACO)
Behavioral sciences
Biomimetics
Cemeteries
Cluster analysis
Clustering
Clustering algorithms
Collaborative learning
collaborative learning environment
Convergence
Data analysis
Datasets
Federated learning
Genetic algorithms
Impact analysis
Learning systems
Machine learning
Parameter estimation
Parameter sensitivity
parameters analysis
parameters sensitivity
School environment
Sensitivity analysis
Sorting
ISSN:2169-3536, 2169-3536
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Summary:Clustering analysis is one of the data analysis techniques that organizes items into clusters according to their degrees of similarities. In this context, bio-inspired algorithms have found success in solving clustering problems. Inspired by nature, Ant Colony based Clustering arises from ant colony behavior in organizing nests and clustering ants corpses. Accordingly, several researchers proposed different clustering algorithms that mimic the real ants behavior in forming cemeteries. However, the performance of a given algorithm depends strongly on its parameters settings. Indeed, it holds a large number of adjustable parameters that need to be instantiated by suitable values. In this paper, we study the parameters influence, more precisely the parameter <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> which is responsible for adjusting similarity between objects. In fact, we analyze the impact of <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> values on the performance of some well known Ant Colony based Clustering Algorithms applied to constructing team-works in a collaborative learning environment. After various bench tests, the choice of <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> value is determined based on the best algorithm accuracy for each learning data-set. The experimental results prove that Ant Colony algorithms performance strongly depends on <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula>, especially when applied to large data-sets size. However, <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> has a negligible influence on the algorithm's accuracy when applied to small data-sets size. Obviously, the feature selection step could be ignored since it has a negligible influence on the algorithm performance even with different values of <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula>.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3279723