Data Clustering Using ART-like Neural Networks

This paper is focused on the data clustering using the ART-like neural network. Firstly is concerned with the problems from the theoretical point of view by explaining what the clustering is, giving an idea of accumulation methods and mentioning the place of the neural networks in the data clustering. From among the neural network it is focused on the MF ARTMAP. Firstly it explains the principle of the ART neural networks namely unSupervised ART, Supervised ARTMAP which is created by uniting two ART neural networks using MAPFIELD together with giving the basis of the fuzzy set of the ARTMAP. The aim of the thesis was not only to explain the theory of the MF ARTMAP networks but also to implement them and suggest their improvement. The experiments were evaluated on the data sets circle in square, spiral and economical data. Improvements are related to data which are categorized into two classes. Improvements roots is addition of third class, which contains classified contradictory. Examples, and their subsequently separation into two classes which are concretized whether there are data which belong to the both classes or are contradictory. In case of the multiclasses classification, the functionality of the MF ARTMAP on the economical data is verified by neural network. The results are processed, visualized as well as explained in the individual sections.