Optimized Thresholding on Self Organizing Map for Cluster Analysis: Genetic Algorithm and Simulated Annealing Applications, with JAVA pseudo code - Brossura
Sinossi
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
L'autore
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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Optimized Thresholding on Self Organizing Map for Cluster Analysis
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LAP Lambert Academic Publishing Dez 2015, 2015
ISBN 10: 3848426285
ISBN 13: 9783848426287
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Taschenbuch. Condizione: 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. Codice articolo 9783848426287
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Optimized Thresholding on Self Organizing Map for Cluster Analysis
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Optimized Thresholding on Self Organizing Map for Cluster Analysis
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Optimized Thresholding on Self Organizing Map for Cluster Analysis
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Optimized Thresholding on Self Organizing Map for Cluster Analysis
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch. Codice articolo 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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Taschenbuch. Condizione: 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. Codice articolo 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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Taschenbuch. Condizione: Neu. Optimized Thresholding on Self Organizing Map for Cluster Analysis | Genetic Algorithm and Simulated Annealing Applications, with JAVA pseudo code | Ehsan Mohebi | Taschenbuch | 124 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783848426287 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 106584904
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Optimized Thresholding on Self Organizing Map for Cluster Analysis: Genetic Algorithm and Simulated Annealing Applications, with JAVA pseudo code
Editore:
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3848426285
ISBN 13: 9783848426287
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