The paper describes the implementation and the functioning of RAHGA (Rule Acquisition with an Entropy-based Genetic Algorithm), a genetic-algorithm-based data mining system suitable for both supervised and certain types of unsupervised knowledge extraction from large and possibly noisy databases. RAHGA differs from a standard Genetic Algorithm. We compared our approach with several other traditional data mining techniques. The results show that our approach outperformed others...
