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Mutlidimensional Detective Alfred Inselberg Streeable, Progressive, Mutlidimensional Scaling Matt Williams, Tamara Munzner Rylan Cottrell
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Mutlidimensional Detective Transformation of multivariate relations into 2-D patterns A discovery process for visual data mining
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Parallel Coordinates Visualize without loss of information. Properties Low complexity. # Dimensions = # Variables Works for any # of dimensions Variables treated uniformly N - dimensional Object Recognized under projective transformations. Conveys information on the properties Based on rigorous math/algo results.
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DON’T PANIC
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Data 473 batches of processors 16 variables X1 - % of yield X2 - quality X3... X12 - are different types of defects X13... X16 - denote a physical parameter
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Maximize yield and quality
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Batches with the highest quality
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Portion of Slovenia
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Satellite Data B1..B5, B7 - Intensity of reflected electromagnetic wavelengths B6 - Intensity of emitted thermal IR from object X,Y - Map Position
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Portion of Slovenia
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Multidimensional Scaling Create a low dimensional layout of data Distance between points best represents the points in higher dimensional data.
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Steerable, Progressive MDS Problem - No Interactive exploration of high-dimensional data sets Unreasonable time cost associated data sets that are large in dimensions and points Steering - focuses computational power
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Layout a random subset of the data set
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Divide bin in two Apply high-dimensional distance
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A new random subset of points are added into the layout
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Focus is placed on user defined bin
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A new subset of random points selected from the unplaced points in the selected region are added
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The process is repeated as the user refines his selection
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MDSteer Standard Layout (Morrison) 50,000 data points
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http://www.cs.ubc.ca/~tmm/papers/mdsteer/videos/MDSteer1.mov
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Standard Layout (Morrison) MDSteer 40,000 data points
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http://www.cs.ubc.ca/~tmm/papers/mdsteer/videos/MDSteer2Combined.mov
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References Alfred Inselberg: The Automated Multidimensional Detective. INFOVIS 1997: 107-114 Alfred Inselberg: Parallel Coordinates: Visuak Multidimensional Geometry and its Applications. 2004. http://www.math.tau.ac.il/~aiisreal/index_files/lect-pdf/lect-intro.pdf http://www.math.tau.ac.il/~aiisreal/index_files/lect-pdf/lect-intro.pdf Matt Williams, Tamara Munzner: Steerable, Progressive Multidimensional Scaling. INFOVIS 2004: 57-64. Project website http://www.cs.ubc.ca/labs/imager/tr/2004/mdsteer/INFOVIS 2004 Matt Williams: QuestVis and MDSteer: The Visualization of High-Dimensional Environmental Sustainability Data. MSc. Thesis. 2004.
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