Automated ontology construction for unstructured text documents
Data & Knowledge Engineering
Failure prediction with self organizing maps
Expert Systems with Applications: An International Journal
Country corruption analysis with self organizing maps and support vector machines
WISI'06 Proceedings of the 2006 international conference on Intelligence and Security Informatics
Category labelling for automatic classification scheme generation
FDIA'07 Proceedings of the 1st BCS IRSG conference on Future Directions in Information Access
Knowledge discovery in inspection reports of marine structures
Expert Systems with Applications: An International Journal
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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.