A fuzzy self-organizing map neural network for market segmentation of credit card

Up to date, the proposed clustering analysis methods are tremendous. In most of the methods, however, human-made determination like the number of clustering groups should be decided previously. Not only will the result be affected by the subjective point of view of the decision-maker, but also clustering efficiency is not good enough. To overcome these drawbacks, this research attempts to combine fuzzy sets theory with the unsupervised learning network model to create an unsupervised Fuzzy Self-Organizing Map (FSOM) model. This model integrates artificial neural network with fuzzy sets theory to take respective advantages of learning function and the capability of handling uncertainty problem in human recognition process. Generally, the fuzzy clustering analysis model developed in the research can completely explain the results from the experiments. Besides, this model seems more useful and practical than other clustering methods. The integration of FSOM and BPN networks to establish an intelligent decision support system can improve the problem of being unable to quickly analyze a new customer information and effectively response a suggestion to the decision maker.