Several authors have developed automated parameterized visualization generation systems14,15,16. All generate classic
visualizations or combinations of such visualizations. A vector space model of visualization was proposed by Hoffman18,
leading to the development of new visualizations and the concept of interpolating visualizations. These new
visualizations provide alternative representations and insights into data and have been applied successfully in numerous
data analysis problems including gene expression, drug discovery, clinical trials, toxicogenomics, and medical
informatics23. In this paper we elevate this vector space model to include analytic visualizations, ones with tightly
coupled analysis, such as Self-Organizing Maps (SOMs) and Multi-Dimensional Scaling (MDS). We describe our new
model and provide an example interpolation of a SOM and a scatterplot with a simple data set (the Fisher Iris data) and a
more complex and larger one (microarray gene expression data).
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