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Optimized Thresholding on Self Organizing Map for Cluster Analysis: Genetic Algorithm and Simulated Annealing Applications, with JAVA pseudo code - Couverture souple
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Optimized Thresholding on Self Organizing Map for Cluster Analysis 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...
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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.
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- ÉditeurLAP LAMBERT Academic Publishing
- Date d'édition2012
- ISBN 10 3848426285
- ISBN 13 9783848426287
- ReliureBroché
- Nombre de pages124
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Description du livre Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 124 pp. Englisch. N° de réf. du vendeur 9783848426287
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Optimized Thresholding on Self Organizing Map for Cluster Analysis Genetic Algorithm and Simulated Annealing Applications, with JAVA pseudo code
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ISBN 10 : 3848426285
ISBN 13 : 9783848426287
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Optimized Thresholding on Self Organizing Map for Cluster Analysis : Genetic Algorithm and Simulated Annealing Applications, with JAVA pseudo code
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LAP Lambert Academic Publishing
(2012)
ISBN 10 : 3848426285
ISBN 13 : 9783848426287
Neuf
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Description du livre Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783848426287
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Optimized Thresholding on Self Organizing Map for Cluster Analysis Genetic Algorithm and Simulated Annealing Applications, with JAVA pseudo code
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(2012)
ISBN 10 : 3848426285
ISBN 13 : 9783848426287
Neuf
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Description du livre PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9783848426287
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Description du livre Etat : New. pp. 124. N° de réf. du vendeur 26128872163
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