SUPERVISED ADAPTIVE CLUSTERING - A HYBRID NEURAL-NETWORK CLUSTERING-ALGORITHM

A neural network architecture is introduced which implements a supervi sed clustering algorithm for the classification of feature vectors. Th e network is self-organising, and is able to adapt to the shape of the underlying pattern distribution as well as detect novel input vectors during training. It is also capable of determining the relative impor tance of the feature components for classification. The architecture i s a hybrid of supervised and unsupervised networks, and combines the s trengths of three well-known architectures: learning vector quantisati on, back-propagation and adaptive resonance theory. Network performanc e is compared to that of learning vector quantisation, back-propagatio n and cascade-correlation. it is found that performance is generally a s good as or better than the performance of these other architectures, while training time is considerably shorter. However the main advanta ge of the hybrid architecture is its ability to gain insight into the feature pattern space.