Clustering decisions frequently arise in business applications such as recommendations concerning products, markets, human resources, etc. Currently, decision makers must analyze diverse algorithms and parameters on an individual basis in order to establish preferences on the decision-making issues they face; because there is no supportive model or tool which enables comparing different result-clusters generated by these algorithms and parameters combinations. The Multi-Algorithm-Voting (MAV) methodology enables not only visualization of results produced by diverse clustering algorithms, but also provides quantitative analysis of the results. The current research applies MAV methodology to the case of recommending new-car pricing. The findings illustrate the impact and the benefits of such decision support system.