Abstract
This chapter introduces a novel systematization aiming at extending the application range of Information Visualization and Visual Data Mining. We present an innovative framework named Visualization Tree in order to integrate multiple data visualizations assisted by novel visual exploration techniques. These exploration techniques are named Frequency Plot, Relevance Plot and Representative Plot, and are integrated according the proposed Visualization Tree framework. The systematization of visualization techniques enabled by these concepts defines a Visual Data Mining environment where multiple presentation workspaces are kept together, linked according to analytical decisions taken by the user. Our emphasis is on developing an intuitive and versatile multiple-views system that helps the user to identify visual patterns while interpreting multiple data subsets. In this context, the analyst is able to draw and summarize several subsets that are inspected simultaneously each in a dedicated workspace.
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Rodrigues, J.F., Traina, A.J.M., Traina, C. (2008). Mining Patterns for Visual Interpretation in a Multiple-Views Environment. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds) Visual Data Mining. Lecture Notes in Computer Science, vol 4404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71080-6_13
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DOI: https://doi.org/10.1007/978-3-540-71080-6_13
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