A general neural framework for classification rule mining

Neural network technology has already been applied in a variety of domains with remarkable success. However, it has not been well utilized in data mining and knowledge discovery. In this paper, a general neural framework named NEUCRUM is proposed for classification rule mining. This paper also presents a possible implementation of NEUCRUM whose key components are a specific neural classifier named FANNC and a novel rule extraction approach named STARE. FANNC is used to learn from pre-processed data, in which its fast learning speed and strong generalization ability are quite contributive. STARE is proposed in this paper, which is used to extract comprehensible, compact and accurate symbolic rules from trained neural networks so that the knowledge discovered is explicitly available to decision-makers. Applications show that NEUCRUM and its implementation described in this paper work well in many real domains.