Content-based software classification by self-organization
Dieter Merkl · 2002
This paper is concerned with a case study in content-based classification of textual documents. In particular we compare the application of two prominent self-organizing neural networks to the same problem domain, namely the organization of software libraries. The two models are adaptive resonance theory and self-organizing maps. As a result we are able to show that both models successfully arrange software components according to their semantic similarity.