Visualization of multi-algorithm clustering for better economic decisions - The case of car pricing

摘要

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