Adaptive Automata Community Detection and Clustering - A generic methodology - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2007

Adaptive Automata Community Detection and Clustering - A generic methodology

Résumé

We present in this paper a generic methodology based on genetic automata for modelling community detection. With Communities, we deal with dynamic organizations which are self-organized from two aspects, the spatial one and the functional one. We propose in this paper a general methodology which extends cellular based modelling-like Schelling models-to more sophisticated approaches based on agent behavior modelling. These agent behaviors are modelled by automata with multiplicities. These automata-based models allow to define powerful operators like genetic operators and behavioral semi-distance. Mixing these operators, we can propose a complex computing, dealing with spatial selforgnaization coupled with behavioral similarity for adaptive self-organized systems.
Fichier principal
Vignette du fichier
10.1.1.149.1215.pdf (334.47 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03316470 , version 1 (06-08-2021)

Identifiants

  • HAL Id : hal-03316470 , version 1

Citer

Rawan Ghnemat, Cyrille Bertelle, Gérard H E Duchamp. Adaptive Automata Community Detection and Clustering - A generic methodology. World Congress on Engineering 2007 (WCE 2007), IAENG, Jul 2007, Londres, United Kingdom. ⟨hal-03316470⟩
27 Consultations
5 Téléchargements

Partager

Gmail Facebook X LinkedIn More