Abstract
In this study, we proposed a method of factor analysis for a huge database so that not only the independence among the factors can be considered, but also the levels of their importance can be measured. To keep the independence between factors, a statistical correlation analysis and the concept of fuzzy set theory are employed, and to measure the importance of factors a neural-based model is developed. A fuzzy set ‘factors are almost dependent’ is used to measure the degree of dependence between factors, and then a hierarchical clustering method is adopted to detect the dependent factors with an -level dependence. Hence, the independent factors also satisfy the same level of requirement. Then, a supervised feedforward neural network is developed to learn the weights of importance of independent factors. In addition, with the designed hierarchical structure, the proposed model facilitates the extraction of new factors when the information of system is not complete. The applicability of the proposed model is evaluated by two cases of customers’ contribution analysis and churn analysis of a telecom company with 0.08% and 1% error rate.