Rules Extraction from ANN Based on Clustering

We propose a novel algorithm based on clustering to extract rules from artificial neural networks. After networks Beijing trained and pruned successfully, inner-rules are generated by discrete activation values of hidden units. Then, weights between input and hidden units are clustered to decrease the complexity of rules extraction. In clustering phase, the clustered number of weights can be adjusted dynamically according to activation values of their corresponding hidden units. The experimental results demonstrate that this algorithm is effective.