A New Procedure for Unsupervised Clustering Based on Combination of Artificial Neural Networks

Classification methods have become one of the main tools for extracting essential information from multivariate data. New classification algorithms are continuously being proposed and created. This paper presents a classification procedure based on a combination of Kohonen and probabilistic neural networks. Its applicability and efficiency are estimated using model data sets (iris flowers data set, wine data set, data with a two-hierarchical structure), then compared with the traditional clustering algorithms (hierarchical clustering, k-means clustering, fuzzy k-means clustering). The algorithm was designed as M-script in Matlab 7.11b software. It was shown that the proposed classification procedure has a great advantage over traditional clustering methods.