Introduction to Information Visualization Jian Huang CS 594 Spring 2002 This 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 facts Comparison of the Abilities of Humans and Computers Data Visualization Methods Interaction 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 above Dimension 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 Matrices Matrix of scatterplots x ydiagrams of the k dimension data Total of k2 2 k scatterplots Landscapes Prosection Views Matrix of all orthogonal projections where the result of the selected multi dimensional range is colored differently Hyperslice 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 attribute Too many data points Query Dependent Coloring Icon 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 retrieval Chernoff faces Stick 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 the visualization show certain data characteristics Stick Figures Shape Coding Color Icons Color Icon Example Pixel Oriented Techniques Basic Idea each attribute value is represented by one colored pixel the value ranges of the attributes are mapped to a fixed colormap the attribute values for each attribute are presented in separate subwindows Two Categories A common step how to arrange pixels on a 2D screen Sample Space Filling Curves for Query Independent Vis Query Dependent Techniques The Key Query Attribute Query Dependent Techniques All attributes Axis Technique A Comparison Hierarchical Techniques Basic Idea Visualization of the data using a hierarchical partitioning into subspaces Examples Dimensional Stacking LWW90 Worlds within Worlds FB90a b Treemap Shn92 Joh93 Cone Trees RMC91 InfoCube RG93 Dimensional Stacking Partitioning of the n dimensional attribute space in 2dimensional subspaces which are stacked into each other Partitioning of the attribute value ranges into classes The important attributes should be used on the outer levels Adequate especially for data with ordinal attributes of low cardinality Example of Dimensional Stacking Tree Map Screen filling method which uses a hierarchical partitioning of the screen into regions depending on the attribute values The x and y dimension of the screen are partitioned alternately according to the attribute values the attribute value ranges have to be partitioned into classes The attributes used for the partitioning and their ordering are user defined the most important attributes should be used first The color of the regions may correspond to an additional
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