Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCC.2004.843177
Title: Extracting salient dimensions for automatic SOM labeling
Authors: Azcarraga, A.P.
Hsieh, M.-H.
Pan, S.L. 
Setiono, R. 
Keywords: Market segmentation
Self-organizing maps (SOM)
Unsupervised SOM labeling
Issue Date: 2005
Source: Azcarraga, A.P.,Hsieh, M.-H.,Pan, S.L.,Setiono, R. (2005). Extracting salient dimensions for automatic SOM labeling. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 35 (4) : 595-600. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCC.2004.843177
Abstract: 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.
Source Title: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
URI: http://scholarbank.nus.edu.sg/handle/10635/42400
ISSN: 10946977
DOI: 10.1109/TSMCC.2004.843177
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