Optimized Thresholding on Self Organizing Map for Cluster Analysis: Genetic Algorithm and Simulated Annealing Applications, with JAVA pseudo code - Couverture souple

Mohebi, Ehsan

 
9783848426287: Optimized Thresholding on Self Organizing Map for Cluster Analysis: Genetic Algorithm and Simulated Annealing Applications, with JAVA pseudo code
Couverture souple
ISBN 10 : 3848426285 ISBN 13 : 9783848426287
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

One of the popular tools in the exploratory phase of data mining and pattern recognition is the Kohonen Self Organizing Map (SOM). Recently, experiments have shown that to find the ambiguities involved in cluster analysis, it is not necessary to consider crisp boundaries in clustering operations. In this Book, the Incremental Leader algorithm for the thresholding of the SOM (Inc-SOM) is proposed to validate the potential of a crisp clustering algorithm. However, the performance deteriorates when there is overlap between clusters. To overcome the ambiguities in the results of cluster analysis, a rough thresholding for the SOM (Rough-SOM) is proposed. In Rough-SOM, the data is first trained by a SOM neural network, then the rough thresholding, which is a rough set based clustering approach, is applied on the neurons of the SOM. The optimal number of clusters can be found by rough set theory, which groups the neurons into a set of overlapping clusters. An optimization technique is applied during the last stage to assign the overlapped data to the true clusters.

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Présentation de l'éditeur

One of the popular tools in the exploratory phase of data mining and pattern recognition is the Kohonen Self Organizing Map (SOM). Recently, experiments have shown that to find the ambiguities involved in cluster analysis, it is not necessary to consider crisp boundaries in clustering operations. In this Book, the Incremental Leader algorithm for the thresholding of the SOM (Inc-SOM) is proposed to validate the potential of a crisp clustering algorithm. However, the performance deteriorates when there is overlap between clusters. To overcome the ambiguities in the results of cluster analysis, a rough thresholding for the SOM (Rough-SOM) is proposed. In Rough-SOM, the data is first trained by a SOM neural network, then the rough thresholding, which is a rough set based clustering approach, is applied on the neurons of the SOM. The optimal number of clusters can be found by rough set theory, which groups the neurons into a set of overlapping clusters. An optimization technique is applied during the last stage to assign the overlapped data to the true clusters.

Biographie de l'auteur

He received his M. S. degree in Computer Science from University Technology of Malaysia in 2010. Currently, He is PhD student in Information Technology at University of Ballarat, Australia.

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

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3848426285
ISBN 13 
9783848426287
Reliure
Broché
Nombre de pages
124
Coordonnées du fabricant
non disponible
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