Introduction to Information VisualizationGoals of VisualizationComparison of the Abilities of Humans and ComputersData Visualization MethodsInteraction TechniquesMajor CategoriesDimension ReductionOther PreprocessingGeometric TechniquesScatterplot-MatricesLandscapesProsection ViewsHypersliceParallel CoordinatesToo many data points?Query Dependent ColoringIcon-based TechniquesChernoff-facesStick FiguresSlide 20Shape CodingColor IconsColor Icon ExamplePixel-Oriented TechniquesTwo CategoriesSample Space Filling Curves for Query Independent VisQuery Dependent Techniques – The Key Query AttributeQuery Dependent Techniques: All attributesAxis TechniqueA ComparisonHierarchical TechniquesDimensional StackingExample of Dimensional StackingTree MapTree Map ExamplesColored Tree MapCone TreeGraph based techniques2D Graph ExamplesSlide 402D-Graph Drawings3D-Graph DrawingGraph-based TechniquesDistortion TechniquesPerspective WallSlide 46Table LensFisheye ViewHyperbolic Tree3D Hyperbolic RepresentationDynamic / Interaction TechniquesData-to-Visualization MappingDynamic/Interactive ProjectionsDynamic/Interactive FilteringMagic Lenses/Moveable FilterFilter Flow ModelSlide 57Dynamic/Interactive Linking & BrushingComparisonComparison by KeimIntroduction to Information VisualizationJian Huang, CS 594, Spring 2002This set of slides are modified from the tutorial given by Prof. Daniel Keim (Univ. of Halle, Germany) on IEEE Visualization Conference, in October 2001.Goals of Visualization•Explorative Analysis–starting point: data without hypotheses about the data–process: interactive, usually undirected search for structures, trends, etc.–result: visualization of the data, which provides hypotheses about the data•Confirmative Analysis–starting point: hypotheses about the data–process: goal-oriented examination of the hypotheses–result: visualization of the data, which allows the confirmation or rejection of the hypotheses•Presentation–starting point: facts to be presented are fixed a priori–process: choice of an appropriate presentation technique–result: high-quality visualization of the data presenting the factsComparison of the Abilities of Humans and ComputersData Visualization MethodsInteraction Techniques•Distortion–Simple Distortion (e.g., Perspective Wall, Bifocal Lenses, TableLens, Graphical Fisheye Views, ...)–Complex Distortion (e.g., Hyperbolic Repr., Hyperbox, ...)•Dynamic Interaction Techniques–Data-to-Visualization Mapping (e.g., AutoVisual, S Plus, XGobi, IVEE)–Projections (e.g., GrandTour, S Plus, XGobi, ...)–Filtering (Selection, Querying) (e.g., MagicLens, Filter/Flow Queries, InfoCrystal)–Linking & Brushing (e.g., Xmdv-Tool, XGobi, DataDesk, ...)–Zooming (e.g., PAD++, IVEE, DataSpace, ...)–Detail on Demand (e.g., IVEE, TableLens, MagicLens, VisDB, ...)Major Categories•Geometric Techniques: Scatterplots, Landscapes, Projection Pursuit, Prosection Views, Hyperslice, Parallel Coordinates,...•Icon-based Techniques: Chernoff Faces, Stick Figures, Shape-Coding, Color Icons, TileBars, ...•Pixel-oriented Techniques: Recursive Pattern Technique, Circle Segments Technique, Spiral- & Axes-Techniques, ...•Hierarchical Techniques: Dimensional Stacking, Worlds-within-Worlds, Treemap, Cone Trees, InfoCube, ...•Graph-Based Techniques: Basic Graphs (Straight-Line, Poly-line, Curved-Line), Specific Graphs (e.g., DAG, Symmetric, Cluster) …•Hybrid Techniques: arbitrary combinations from aboveDimension Reduction•Set of d-dim Data Items -> Set of k-dim. Data Items; k << d•Principal Component Analysis [DE82]–Determines a minimal set of principal components (linear combinations of the original dimensions) to explain main variations of the data.•Factor Analysis [Har67]–Determines a set of un-observable common factors which explain the main variations of the data. The original dimensions are linear combinations of the common factors.•Multidimensional Scaling [SRN72]–Uses the similarity (or dissimilarity) matrix of the data as defining coordinate axes in multi-dimensional space. The Euclidean distance in that space is a measure of the similarity of the data items.Other Preprocessing•Subsetting: (Set of Data Items -> Subset of Data Items)–Sampling (determines a representative subset of the dbase)–Querying (determines a certain, usually a-priori fixed subset of the database)•Segmentation (Set of Data Items -> Set of (Set of Data Items))–Segmentation based upon attribute values or attribute ranges•Aggregation (Set of Data Items -> Set of Aggregate Values)–Aggregation (sum, count, min, max,...) based upon•attribute values•topological properties, etc.–Visualizations of Aggregations:•Histograms•Pie Charts, Bar Charts, Line Graphs, etc.Geometric Techniques•Basic Idea: Visualization of geometric transformations and projections of the data.•Examples:–Scatterplot-Matrices [And72, Cle93]–Landscapes [Wis95]–Projection Pursuit Techniques [Hub85]•(techniques for finding meaningful projections of multidimensional data)–Prosection Views [FB94, STDS95]–Hyperslice [WL93]–Parallel Coordinates [Ins85, ID90]Scatterplot-MatricesMatrix of scatterplots (x-y-diagrams) of the k-dimension data. Total of (k2/2 - k) scatterplotsLandscapesProsection Views•Matrix of all orthogonal projections where the result of the selected multi-dimensional range is colored differentlyHyperslice•Matrix of k2 slices through the k-dim. data (the slices are determined interactively)Parallel Coordinates•N equidistant axes which are parallel to one of the screen axes and correspond to the attributes•The axes are scaled to the [minimum, maximum] - range of the corresponding attribute•Every data item corresponds to a polygonal line which intersects each of the axes at the point which corresponds to the value for the attributeToo many data points?Query Dependent ColoringIcon-based Techniques•Basic Idea: Visualization of the data values as features of icons.•Examples–Chernoff-Faces [Che73, Tuf83]–Stick Figures [Pic70, PG88]–Shape Coding [Bed90]–Color Icons [Lev91, KK94]–TileBars [Hea95]•use of small icons representing the relevance feature vectors in document retrievalChernoff-facesStick Figures•Visualization of the multidimensional data using stick figure icons•Two attributes of the data are mapped to the display axes and the remaining at-tributes are mapped to the angle and/or length of the limbs•Texture patterns in
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