Externalising abstract mathematical models
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Parallel coordinates: a tool for visualizing multi-dimensional geometry
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XmdvTool: integrating multiple methods for visualizing multivariate data
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VizCraft (case study): a multimensional visualization tool for aircraft configuration design
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Multidimensional information visualization through sliding rods
AVI '00 Proceedings of the working conference on Advanced visual interfaces
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Computing in Science and Engineering
Business information visualization
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Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
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A radial focus+context visualization for multi-dimensional functions
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Geometric visualization of clusters obtained from fuzzy clustering algorithms
Pattern Recognition
Discussion: Interacting with parallel coordinates
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Kaleidomaps: a new technique for the visualization of multivariate time-series data
Information Visualization
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ER '08 Proceedings of the ER 2008 Workshops (CMLSA, ECDM, FP-UML, M2AS, RIGiM, SeCoGIS, WISM) on Advances in Conceptual Modeling: Challenges and Opportunities
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Advances in Engineering Software
VISPLORE: a toolkit to explore particle swarms by visual inspection
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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The display of multivariate datasets in parallel coordinates, transforms the search for relations among the variables into a 2-D pattern recognition problem. This is the basis for the application to visual data mining. The knowledge discovery process together with some general guidelines are illustrated on a dataset from the production of a VLSI chip. The special strength of parallel coordinates is in modeling relations. As an example, a simplified economic model is constructed with data from various economic sectors of a real country. The visual model shows the interelationship and dependencies between the sectors, circumstances where there is competition for the same resource, and feasible economic policies. Interactively, the model can be used to do trade-off analyses, discover sensitivities, do approximate optimization, monitor (as in a process) and provide decision support.