Clustering Nominal and Numerical Data: A ...
Type de document :
Communication dans un congrès avec actes
Titre :
Clustering Nominal and Numerical Data: A New Distance Concept for a Hybrid Genetic Algorithm
Auteur(s) :
Jourdan, Laetitia [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Dhaenens, Clarisse [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Dhaenens, Clarisse [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Éditeur(s) ou directeur(s) scientifique(s) :
Jens Gottlieb and G\"{u}nther R. Raidl
Titre de la manifestation scientifique :
Evolutionary Computation in Combinatorial Optimization -- {EvoCOP}~2004
Ville :
Coimbra, Portugal
Date de début de la manifestation scientifique :
2004-04
Éditeur :
Springer Verlag
Date de publication :
2004-04
Mot(s)-clé(s) en anglais :
evolutionary computation
Discipline(s) HAL :
Mathématiques [math]/Combinatoire [math.CO]
Résumé en anglais : [en]
As intrinsic structures, like the number of clusters, is, for real data, a major issue of the clustering problem, we propose, in this paper, CHyGA (Clustering Hybrid Genetic Algorithm) an hybrid genetic algorithm for ...
Lire la suite >As intrinsic structures, like the number of clusters, is, for real data, a major issue of the clustering problem, we propose, in this paper, CHyGA (Clustering Hybrid Genetic Algorithm) an hybrid genetic algorithm for clustering. CHyGA treats the clustering problem as an optimization problem and searches for an optimal number of clusters characterized by an optimal distribution of instances into the clusters. CHyGA introduces a new representation of solutions and uses dedicated operators, such as one iteration of K-means as a mutation operator. In order to deal with nominal data, we propose a new definition of the cluster center concept and demonstrate its properties. Experimental results on classical benchmarks are given.Lire moins >
Lire la suite >As intrinsic structures, like the number of clusters, is, for real data, a major issue of the clustering problem, we propose, in this paper, CHyGA (Clustering Hybrid Genetic Algorithm) an hybrid genetic algorithm for clustering. CHyGA treats the clustering problem as an optimization problem and searches for an optimal number of clusters characterized by an optimal distribution of instances into the clusters. CHyGA introduces a new representation of solutions and uses dedicated operators, such as one iteration of K-means as a mutation operator. In order to deal with nominal data, we propose a new definition of the cluster center concept and demonstrate its properties. Experimental results on classical benchmarks are given.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Non spécifiée
Vulgarisation :
Non
Collections :
Source :
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