A New Perspective for Neural Networks: Application to a Marketing Management Problem

Abstract

Over the last few years, connectionism or neural networks (NN) have successfully been applied to a wide range of areas and have demonstrated their capabilities in solving complex problems. Current indications show that these techniques are very important and rapidly developing areas of research and applications, particularly, in the area of data mining for knowledge discovery. One particular neural network model, the back-propagation (BP) algorithm, has performed very well in this regard and it is now accepted as a reliable method for data mining. However, these models have their shortcomings. The major difficulty lies in the fact that the relationships between specific variables and the neural network results are, at best, difficult to explain. This article presents an innovative but simple method for using NN to understand the pattern/outcome correlation to interpret a cause and effect relationship. A comparative analysis and experimental results are also presented to show the validity of the proposed scheme.