Applied multivariate statistical analysis
Applied multivariate statistical analysis
A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
A factor spaces approach to knowledge representation
Fuzzy Sets and Systems - Fuzzy information processing
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Data mining solutions: methods and tools for solving real-world problems
Data mining solutions: methods and tools for solving real-world problems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
α-complete information in factor space
IEEE Transactions on Fuzzy Systems
Neural-network feature selector
IEEE Transactions on Neural Networks
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In this study, we propose a method of factor analysis for a huge database so that not only the independence among the factors can be ensured and the levels of their importance can be measured, but also new factors can be discovered. To measure the independence of the factors, statistical relation analysis and the concept of fuzzy set theory are employed. A fuzzy set of 'the factors are almost dependent' is used to measure the degree of dependence between factors, and then through a hierarchical clustering procedure, the dependent factors are detected and removed. To measure the weights of importance for the independent factors, a supervised feedforward neural network is developed. In addition, we design a hierarchical structure to facilitate the extraction of new factors when the information of the system is not complete. The applicability of the proposed model is evaluated by a case of customers' contribution analysis of a telecom company with 8% error rate.