Evaluating Multivariate Visualizations as Multi-objective Decision Aids

Abstract

Abstract. When choosing a solution, decision makers are often required to account for multiple conflicting objectives. This is a situation that can result in a potentially huge number of candidate solutions. Despite the wide selection of multivariate visualizations that can potentially help decide between various candidates, no designated means exist to assess the effectiveness of these visualizations under different circumstances. As a first contribution in this work, we developed a method to evaluate different types of multivariate visualization. The method focuses on the visualization's ability to facilitate a better understanding of inter-objective trade-offs as a proxy to more sensible decision making. We used the method to evaluate two existing visualization aids: ParallelCoordinates and an adaptation of Self Organizing Maps (SOM). Both visualizations were compared with tabular data presentation. Our results show that the first visualization is more effective than a plain tabular visualization for the purpose of multi-objective decision making.

Control Procedures Support System of Investment Project in the Fuel and Energy Complex on the Basis of Self-Organizing Maps

“…The procedure of data analysis and decisionmaking for the upper level of control using Kohonen neural networks is proposed [13,[16][17][18][19][20][21]. The generalized scheme of the proposed procedure is shown in Fig.…”