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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Bibliographic Details
Published in:IEEE transactions on human-machine systems 2005-11, Vol.35 (4), p.595-600
Main Authors: Azcarraga, A.P., Ming-Huei Hsieh, Pan, S.L., Setiono, R.
Format: Article
Language:English
Subjects:
Applied sciences
Backpropagation
Clustering
Computer science
control theory
systems
Control theory. Systems
Cybernetics
Data mining
Exact sciences and technology
Labeling
Learning
Learning systems
Market segmentation
Markets
Marking
Multi-layer neural network
Multilayer perceptrons
Neural networks
Resonance
Segmentation
Self organizing feature maps
self-organizing maps (SOM)
Studies
Subspace constraints
Training
unsupervised SOM labeling
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Summary: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.
ISSN:1094-6977
2168-2291
1558-2442
2168-2305
DOI:10.1109/TSMCC.2004.843177