INVESTIGATION OF DATA MINING USING PRUNED ARTIFICIAL NEURAL NETWORK TREE

A major drawback associated with the use of artificial neural networks for datamining is their lack of explanation capability. while they can achieve a highpredictive accuracy rate, the knowledge captured is not transparent and cannotbe verified by domain experts. in this paper, artificial neural network tree(annt), i.e. ann training preceded by decision tree rules extraction methodis presented to overcome the comprehensibility problem of ann. two pruningtechniques are used with the annt algorithm; one is to prune the neuralnetwork and another to prune the tree. both of these pruning methods areevaluated to see the effect on annt in terms of accuracy, comprehensibilityand fidelity.