Extracting salient dimensions for automatic SOM labeling

College

College of Computer Studies

Department/Unit

Computer Technology

Document Type

Article

Source Title

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volume

35

Issue

4

First Page

595

Last Page

600

Publication Date

11-1-2005

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.

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Digitial Object Identifier (DOI)

10.1109/TSMCC.2004.843177

Disciplines

Computer Sciences

Keywords

Self-organizing maps; Market segmentation

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