Extracting salient dimensions for automatic SOM labeling
Learning in self-organizing maps (SOM) is considered unsupervised because training patterns do not need accompanying desired output information. Prior to its use in some real-world applications, however, a trained SOM often has to be labeled. This labeling phase is usually supervised in that labeled...
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oai:animorepository.dlsu.edu.ph:faculty_research-15042021-11-26T03:49:34Z Extracting salient dimensions for automatic SOM labeling Azcarraga, Arnulfo P. Hsieh, Ming Huei Pan, Shah L. Setiono, Rudy Learning in self-organizing maps (SOM) is considered unsupervised because training patterns do not need accompanying desired output information. Prior to its use in some real-world applications, however, a trained SOM often has to be labeled. This labeling phase is usually supervised in that labeled patterns need accompanying output information. Because such labeled patterns are not always available or may not even be possible to construct, the supervised nature of the labeling phase restricts the deployment of SOM from a wide range of potential domains of application. This work proposes a methodical and automatic SOM labeling procedure that does not require a set of prelabeled patterns. Instead, nodes in the trained map are clustered and subsets of training patterns associated to each of the clustered nodes are identified. Salient dimensions per node cluster that constitute the bases for labeling each node in the map are then identified. The effectiveness of the method is demonstrated on a SOM-based international market segmentation study. © 2005 IEEE. 2005-11-01T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/505 https://animorepository.dlsu.edu.ph/context/faculty_research/article/1504/type/native/viewcontent Faculty Research Work Animo Repository Self-organizing maps Market segmentation Computer Sciences |
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Self-organizing maps Market segmentation Computer Sciences Azcarraga, Arnulfo P. Hsieh, Ming Huei Pan, Shah L. Setiono, Rudy Extracting salient dimensions for automatic SOM labeling |
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Learning in self-organizing maps (SOM) is considered unsupervised because training patterns do not need accompanying desired output information. Prior to its use in some real-world applications, however, a trained SOM often has to be labeled. This labeling phase is usually supervised in that labeled patterns need accompanying output information. Because such labeled patterns are not always available or may not even be possible to construct, the supervised nature of the labeling phase restricts the deployment of SOM from a wide range of potential domains of application. This work proposes a methodical and automatic SOM labeling procedure that does not require a set of prelabeled patterns. Instead, nodes in the trained map are clustered and subsets of training patterns associated to each of the clustered nodes are identified. Salient dimensions per node cluster that constitute the bases for labeling each node in the map are then identified. The effectiveness of the method is demonstrated on a SOM-based international market segmentation study. © 2005 IEEE. |
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text |
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Azcarraga, Arnulfo P. Hsieh, Ming Huei Pan, Shah L. Setiono, Rudy |
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Azcarraga, Arnulfo P. Hsieh, Ming Huei Pan, Shah L. Setiono, Rudy |
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Azcarraga, Arnulfo P. |
title |
Extracting salient dimensions for automatic SOM labeling |
title_short |
Extracting salient dimensions for automatic SOM labeling |
title_full |
Extracting salient dimensions for automatic SOM labeling |
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Extracting salient dimensions for automatic SOM labeling |
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Extracting salient dimensions for automatic SOM labeling |
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extracting salient dimensions for automatic som labeling |
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Animo Repository |
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2005 |
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https://animorepository.dlsu.edu.ph/faculty_research/505 https://animorepository.dlsu.edu.ph/context/faculty_research/article/1504/type/native/viewcontent |
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