Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/123118
Título: Parameters Sensitivity Analysis of Ant Colony based Clustering: Application for Student Grouping in Collaborative Learning Environment
Autores/as: Abid, Abir
Kallel, Ilhem
Ayed, Mounir Ben
Sanchez-Medina, Javier J. 
Clasificación UNESCO: 2404 Biomatemáticas
Palabras clave: Clustering algorithms
Genetic algorithms
Behavioral sciences
Parameter estimation
Learning systems
Fecha de publicación: 2023
Publicación seriada: IEEE Access 
Resumen: 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 α which is responsible for adjusting similarity between objects. In fact, we analyze the impact of α 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 α 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 α, especially when applied to high-dimensional data-sets. However, α has a negligible influence on the algorithm’s accuracy when applied to low-dimensional data-sets. Obviously, the feature selection step could be ignored since it has a negligible influence on the algorithm performance even with different values of α.
URI: http://hdl.handle.net/10553/123118
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2023.3279723
Fuente: IEEE Access[EISSN 2169-3536], (Enero 2023)
Colección:Artículos
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