Optimisation de Fuzzy C-Means (FCM) clustering par la méthode des directions alternées (ADMM)
| Autor: | Albert, Benoit, Antoine, Violaine, Koko, Jonas |
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| Přispěvatelé: | Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA) |
| Jazyk: | francouzština |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Revue des Nouvelles Technologies de l'Information Extraction et Gestion des Connaissances (EGC) Extraction et Gestion des Connaissances (EGC), 2023, Lyon, France. pp.247-258 |
| Popis: | International audience; Among the clustering methods, K-Means and variants are very popular. Thesemethods solve at each iteration the first order optimality conditions. However, in somecases, the function to be minimized is not convex, as for the Fuzzy C-Means version withMahalanobis distance (FCM-GK). In this study, we apply the Alternating DirectionsMethod of Multiplier (ADMM) to ensure a good convergence. ADMM is often appliedto solve a separable convex minimization problem with linear constraints. ADMM isa decomposition/coordination method with a coordination step provided by Lagrangemultipliers. By appropriately introducing auxiliary variables, this method allows theproblem to be decomposed into easily solvable convex subproblems while keeping thesame iterative structure. Numerical results have demonstrated the significant perfor-mance of the proposed method compared to the standard method especially for highdimensional data. |
| Databáze: | OpenAIRE |
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