Data mining with self generating neuro-fuzzy classifiers
Damminda Alahakoon, Saman Kumara Halgamuge, Balakrishnan Srinivasan · 1999
Self generating neural networks have been presented as a better alternative to fixed structure networks in data mining applications. It has also been shown that the nearest prototype classifier is functionally equivalent to an alternative fuzzy classifier model. Several supervised neural networks have been developed to generate nearest prototypes which can be converted to fuzzy rules. We present an extended version of our growing self-organising map (GSOM) model which can also be used to identify nearest prototypes for generating fuzzy rules.