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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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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Abstract	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>.
AbstractList	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 [Formula Omitted] which is responsible for adjusting similarity between objects. In fact, we analyze the impact of [Formula Omitted] 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 [Formula Omitted] 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 [Formula Omitted], especially when applied to large data-sets size. However, [Formula Omitted] 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 [Formula Omitted]. 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 <tex-math notation="LaTeX">$\alpha $ </tex-math> which is responsible for adjusting similarity between objects. In fact, we analyze the impact of <tex-math notation="LaTeX">$\alpha $ </tex-math> 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 <tex-math notation="LaTeX">$\alpha $ </tex-math> 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 <tex-math notation="LaTeX">$\alpha $ </tex-math>, especially when applied to large data-sets size. However, <tex-math notation="LaTeX">$\alpha $ </tex-math> 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 <tex-math notation="LaTeX">$\alpha $ </tex-math>. 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>.
Author	Kallel, Ilhem Abid, Abir Ayed, Mounir Ben Sanchez-Medina, Javier J.
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References	ref13 ref12 ref15 Kennedy (ref4); 4 ref14 ref31 ref30 ref11 ref33 ref32 Carlisle (ref10); 1 ref2 ref1 ref16 ref19 ref18 Boryczka (ref17) 2008; 1998 Teodorovic (ref7) 2005; 51 ref24 Deneubourg (ref29) ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref8 ref9 Cortez (ref23) ref3 ref6 ref5
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SubjectTerms	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
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Title	Parameters Sensitivity Analysis of Ant Colony Based Clustering: Application for Student Grouping in Collaborative Learning Environment
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