Visualization and Decision-Making using Structural Information

Visualization and computational intelligence methods such as data mining and knowledge discovery are important tools for decision support in imaging science. The goal of this paper is to discuss the blending of these areas to improve decision making for a variety of applications. The paper offers a conceptual model for integrating decision-making models, models for discovery of relations (computational intelligence models) and methods of visual correlation. Analysts use visual correlation for discovering relations and decision-makers use them for visualizing the decision under consideration. The model reflects this difference. The blending of these models is illustrated in the example of stopping cholera in London in the 19 century. The last part of the paper is devoted to systematization of visual correlation (VC) methods and their efficiency.