scholarly journals Automated knowledge acquisition for strategic knowledge

1989 ◽  
Vol 4 (3-4) ◽  
pp. 293-336 ◽  
Author(s):  
Thomas R. Gruber
Author(s):  
Nazar Elfadil ◽  

Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.


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