Visualizing Concept Associations Using Concept Density Maps

The concept mapping algorithm proposed in an earlier paper is one of the dimensionality reduction techniques that can be used for knowledge domain visualization. Using this algorithm to visualize large knowledge domains may not always provide a good overview of the domain due to visual cluttering of concepts. In this paper, we propose to apply kernel density estimation to the visualization of concept maps in order to be able to better explore large knowledge domains. Kernel density estimation proves to be useful for the identification of concept clusters at different levels of detail. In addition to the visual exploration of large knowledge domains, we are also able to visually verify the hypothesis that the concept mapping algorithm places related concepts close to each other. The flexibility and effectiveness of our approach is validated by applying the proposed technique to different visualization scenarios for the field of computational intelligence