Developing a good clustering technique is a challenging task in data mining. Several models, such as artificial neural network approaches and genetic algorithms, represent a promising approach to address this challenge. This paper presents an experiment comparing a map-based training method used for clustering with a traditional clustering method. We test the effectiveness of both methods in helping decision makers in the information systems domain. Our analysis shows that the experimental group outperformed the controllable group. Statistical evidence shows that both training approaches are effective and present good results. Recommendations are made to researchers and practitioners to improve the efficacy of the map-based training approach.