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Effective Data Mining Using Neural Networks
December 1996 (vol. 8 no. 6)
pp. 957-961

Abstract—Classification is one of the data mining problems receiving great attention recently in the database community. This paper presents an approach to discover symbolic classification rules using neural networks. Neural networks have not been thought suited for data mining because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by humans. With the proposed approach, concise symbolic rules with high accuracy can be extracted from a neural network. The network is first trained to achieve the required accuracy rate. Redundant connections of the network are then removed by a network pruning algorithm. The activation values of the hidden units in the network are analyzed, and classification rules are generated using the result of this analysis. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of standard data mining test problems.

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Index Terms:
Data mining, neural networks, rule extraction, network pruning, classification.
Citation:
Hongjun Lu, Rudy Setiono, Huan Liu, "Effective Data Mining Using Neural Networks," IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 957-961, Dec. 1996, doi:10.1109/69.553163
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