Data Mining Based on Computational Intelligence

This paper combines computational intelligence tools: neural network, fuzzy logic, and genetic algorithm to develop a data mining architecture (NFGDM). which discovers patterns and represents them in understandable forms. In the NFGDM. input data are preprocessed by fuzzification, the preprocessed data of input variables are then used to train a radial Basis probabilistic neural network to classify the dataset according to the classes considered. A rule extraction technique is then applied in order to extract explicit knowledge from the trained neural networks and represent it in the form of fuzzy if-then rules. In the final stage, genetic algorithm is used as a rule-pruning module to eliminate those weak rules that are still in the rule bases. Comparison with some known neural network classifier, the architecture has fast learning speed, and it is characterized hy the incorporation of the possibillty information into the consequents of classification rules in human understandable forms. The experiments show that the NFGDM is more efficient and more robust than traditional decision tree method.

Data mining based on computational intelligence

This paper combines computational intelligence tools: neural network, fuzzy logic, and genetic algorithm to develop a data mining architecture (NFGDM), which discovers patterns and represents them in understandable forms. In the NFGDM, input data are preprocessed by fuzzification, the preprocessed data of input variables are then used to train a radial basis probabilistic neural network to classify the dataset according to the classes considered. A rule extraction technique is then applied in order to extract explicit knowledge from the trained neural networks and represent it in the form of fuzzy if\|then rules. In the final stage, genetic algorithm is used as a rule\|pruning module to eliminate those weak rules that are still in the rule bases. Comparison with some known neural network classifier, the architecture has fast learning speed, and it is characterized by the incorporation of the possibility information into the consequents of classification rules in human understandable forms. The experiments show that the NFGDM is more efficient and more robust than traditional decision tree method.