Self-organizing neural networks for unsupervised pattern recognition
Dongyoun Kim, Terrance L. Huntsberger · 2002
A self-organizing neural network model is proposed for pattern classification for any given data sets without a priori information about the number of clusters or cluster centers. This system is based on extensions of Kohonen's self-organizing feature map model. It adjusts weights between the input layer and the distance layer until it is stabilized by utilizing feedback information for specific neurons. Then it classifies each input according to the distance between the weights and the normalized input using J.C. Bezdek's (1981) membership value equation. The final weights between the input layer and the distance layer become the coordinates of the cluster centers. Some experimental studies are presented for a sample data set and a color image.>