Authors propose a new approach in the optimization of inference processes in decision support systems with incomplete knowledge. The idea is based on clustering large set of rules from knowledge bases as long as it is necessary to find a relevant rule as quickly as possible. This work is highly focused on the results of experiments regarding the influence of Agnes’ algorithm parameters on the quality of the clustering process. Additionally, the authors present the results of the experiments regarding the optimal amount of groups formed by decision rules.