Using neural networks for marketing research data classification
Jiří Šťastný, Pavel Turčínek, Arnošt Motyčka · International Conference on Mathematical and Computational Methods in Science and Engineering · 2011
This paper deals with problems of marketing research data classification by means of artificial Neural Networks algorithms. Two basic methods are described, classification with the aid of Multi-layer Perceptron neural network with Back-propagation algorithm and classification with the aid of Self-organizing (Kohonen's) maps. Finally, applicability of these algorithms is compared. These algorithms are applied over the data from a survey about consumer behavior in the food market in the Czech Republic. The limits of this approach are considered and possibilities of more complex structural recognition methods and semi-supervised learning utilization are suggested.