One of the important discussions in data mining is extracting effective and useful rules from the great set of datasets. So, we should follow set of features that at first; are without any noise; secondly, having a little correlation with other features. In other words, we should use instances that are distinctive with other features. So, in this paper we present a combined approach to consider how factors such as distinct features and instances are useful for extracting the rules. In this approach we used a trained neural network to explore useful features, clustering to find out the best instances from dataset and finally we used artificial immune system for rules extraction. In order to evaluating of our introduced approach, we applied it on the UCI dataset of breast cancer diagnosis. Our experiments demonstrate that the combined proposed approach generates reliable rules and contributes more accuracy eventually; these results show the proposed method has %5.9 better accuracy relative to CART method.

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