A novel clustering-neural tree for pattern classification

When performing classification of large set of samples, neural tree classifiers (NTs) are preferred. However, the classical NTs have poor generalization properties. So, in this paper we propose a new classification method referred to as clustering-neural tree classifier, combining clustering technique with neural networks. It can be well applied to classifications of large set of samples, while having good generalization properties. The experimental results on the two spirals problem and the iris problem show that our proposed NN-tree classifier is effective and efficient.