Unsupervised Connectionist Algorithms for Clustering an environmental data set : a comparison
Identifieur interne : 00B309 ( Main/Merge ); précédent : 00B308; suivant : 00B310Unsupervised Connectionist Algorithms for Clustering an environmental data set : a comparison
Auteurs : Laurent Bougrain [France] ; Frédéric Alexandre [France]Source :
- Neurocomputing [ 0925-2312 ] ; 1999.
Descripteurs français
- mix :
Abstract
Various unsupervised algorithms for vector quantization can be found in the literature. Being based on different assumptions, they do not all yield exactly the same results on the same problem. To better understand these differences, this article presents an evaluation of some unsupervised neural networks, considered among the most useful for quantization, in the context of a real-world problem: radioelectric wave propagation. Radio wave propagation is highly dependent upon environmental characteristics (e.g. those of the city, country, mountains, etc.). Within the framework of a cell net planning its radiocommunication strategy, we are interested in determining a set of environmental classes, sufficiently homogeneous, to which a specific prediction model of radio electrical field can be applied. Of particular interest are techniques that allow improved analysis of results. Firstly, Mahalanobis' distance, taking data correlation into account, is used to make assignments. Secondly studies of class dispersion and homogeneity, using both a data structure mapping representation and statistical analysis, emphasize the importance of the global properties of each algorithm. In conclusion, we discuss the advantages and disadvantages of each method on real problems.
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type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic><series><title level="j">Neurocomputing</title><idno type="ISSN">0925-2312</idno><imprint><date type="datePub">1999</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>apprentissage non supervisé</term><term>neural networks</term><term>prototypage</term><term>radio-communication</term><term>radiocommunication</term><term>réseaux de neurones</term><term>unsupervised learning</term><term>vector quantization</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Various unsupervised algorithms for vector quantization can be found in the literature. Being based on different assumptions, they do not all yield exactly the same results on the same problem. To better understand these differences, this article presents an evaluation of some unsupervised neural networks, considered among the most useful for quantization, in the context of a real-world problem: radioelectric wave propagation. Radio wave propagation is highly dependent upon environmental characteristics (e.g. those of the city, country, mountains, etc.). Within the framework of a cell net planning its radiocommunication strategy, we are interested in determining a set of environmental classes, sufficiently homogeneous, to which a specific prediction model of radio electrical field can be applied. Of particular interest are techniques that allow improved analysis of results. Firstly, Mahalanobis' distance, taking data correlation into account, is used to make assignments. Secondly studies of class dispersion and homogeneity, using both a data structure mapping representation and statistical analysis, emphasize the importance of the global properties of each algorithm. In conclusion, we discuss the advantages and disadvantages of each method on real problems.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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