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Optimized Thresholding on Self Organizing Map for Cluster Analysis: Genetic Algorithm and Simulated Annealing Applications, with JAVA pseudo code - Softcover
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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About the Author:
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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- PublisherLAP LAMBERT Academic Publishing
- Publication date2012
- ISBN 10 3848426285
- ISBN 13 9783848426287
- BindingPaperback
- Number of pages124
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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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Optimized Thresholding on Self Organizing Map for Cluster Analysis
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Book Description Taschenbuch. Condition: 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. Seller Inventory # 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 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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ISBN 13: 9783848426287
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Book Description Taschenbuch. Condition: 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. Seller Inventory # 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 13: 9783848426287
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Book Description Condition: New. Print on Demand pp. 124 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam. Seller Inventory # 131715388
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