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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Veröffentlicht in:	IEEE access Jg. 12; S. 24751 - 24761
Hauptverfasser:	Abid, Abir, Kallel, Ilhem, Sanchez-Medina, Javier J., Ayed, Mounir Ben
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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