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Fuzzy Clustering using Levenberg-Marquardt Optimization and Deterministic Anneal - Couverture souple

Abadpour, Arash

 
9781540540164: Fuzzy Clustering using Levenberg-Marquardt Optimization and Deterministic Anneal
Couverture souple
ISBN 10 : 1540540162 ISBN 13 : 9781540540164
Editeur : CreateSpace Independent Publishing Platform, 2016

Synopsis

In this paper, we address the generic problem of unsupervised data clustering within a fuzzy framework. We show that this problem can be formulated as a least mean square vector cost function and propose an iterative solution procedure which utilizes the Levenberg-Marquardt optimization technique. As such, we develop an algorithm which is capable of performing the stated task through utilizing abstract notions of data item, cluster, and data item-to-cluster distance. We demonstrate that the fuzzifier can be utilized as a temperature factor, in order to set up a Deterministic Annealing framework that provides assurance that the derived loss function converges to an acceptable minimum. This is important because the developed method includes an additional layer of numerical optimization on top of the structure commonly used in the literature. This paper includes the derivation of the cost function as well as the outline of the developed solution procedure. We carry sample problem instances from six problem classes and discuss different aspects of the developed method. Finally, we provide a potential next step for this work.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

In this paper, we address the generic problem of unsupervised data clustering within a fuzzy framework. We show that this problem can be formulated as a least mean square vector cost function and propose an iterative solution procedure which utilizes the Levenberg-Marquardt optimization technique. As such, we develop an algorithm which is capable of performing the stated task through utilizing abstract notions of data item, cluster, and data item-to-cluster distance. We demonstrate that the fuzzifier can be utilized as a temperature factor, in order to set up a Deterministic Annealing framework that provides assurance that the derived loss function converges to an acceptable minimum. This is important because the developed method includes an additional layer of numerical optimization on top of the structure commonly used in the literature. This paper includes the derivation of the cost function as well as the outline of the developed solution procedure. We carry sample problem instances from six problem classes and discuss different aspects of the developed method. Finally, we provide a potential next step for this work.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
CreateSpace Independent Publishing Platform
Date d'édition
2016
Langue
anglais
ISBN 10 
1540540162
ISBN 13 
9781540540164
Reliure
Broché
Nombre de pages
56
Coordonnées du fabricant
Manufactured by Amazon on behalf of the author
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