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Data Visualization
Visualization: The use of computer-supported, interactive,
visual representations of data to amplify cognition.
Information Visualization: The use of computer-supported,
interactive visual representations of abstract data to amplify
cognition.S. Card
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Data Visualization
Brief History Key TechniquesScience versus Aesthetics
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Data Visualization: Brief HistoryLiterature Overview:
Jacques BertinSemiology of Graphics: Diagrams, Networks, Maps,
1983
coined the term using vision to think
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Data Visualization: Brief HistoryLiterature Overview:
Edward TufteThe Visual Display of Quantitative Information,
2001Envisioning Information, 1990 Visual Explanations: Images and
Quantities, Evidence and Narrative, 1997The Cognitive Style of
PowerPoint, 2006
Keywords: visualization of statistical data, cartograms, history
of information visualization, visualization displays, micro and
macro readings, small multiples, escaping flatland
Tufte Home PageTufte Article on Stanford Alumni Magazine
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Data Visualization: Brief HistoryLiterature Overview:
maximization of useful information on a limited display
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Data Visualization: Brief HistoryLiterature Overview:
Chaomei ChenInformation Visualization: Beyond the Horizon,
2004Mapping Scientific Frontiers: The Quest for Knowledge
Visualization, 2003Information Visualisation and Virtual
Environments, 1999CiteSpace II: Detecting and visualizing emerging
trends and transient patterns in scientific literature, 2006Top 10
unsolved information visualization problems, 2005 (pdf)
Keywords: domain visualization, network/graph visualizations,
visualization in virtual (collaborative) environments, social
networks
Chaomei Chen Home Page
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Data Visualization: Brief HistoryLiterature Overview:
Ben ShneidermanThe Craft of Information Visualization: Readings
and Reflections, 2003Readings in Information Visualization: Using
Vision to Think, 1999
Keywords: human factors, HCIL, visual dynamic query tools,
social networks
Film FinderBen Shneiderman on Social Networks
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Data Visualization: Brief HistoryLiterature Overview:
Stuart CardA Framework for Visualization, 2002The Internet Edge:
Social, Technical, and Legal Challenges for a Networked World,
2000Readings in Information Visualization: Using Vision to Think,
1999The Structure of the Information Visualization Design Space
(survey paper on evaluation)
Keywords: HCI, Model Human Processor, GOMS (goals, operators,
methods, and selection rules) theory of user interaction,
information foraging theory, statistical descriptions of Internet
use
Stuart Card BioGOMS
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Data Visualization: Brief HistoryLiterature Overview:
More on HCI:AffordancesHicks LawFitts LawFive Hat RacksUsability
EngineeringEvaluation
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Data Visualization: ScientificLiterature Overview:
GIS, Geographic Data VisualizationsTherese-Marie RhyneDaniel
KeimAlan MacEachrenWaldo Tobler (cartograms survey paper)Andre
Skupin (cartograms & perception)
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Data Visualization: ScientificLiterature Overview:
Network / Graph VisualizationPeter EadesThomas Fruchterman,
Edward ReingoldTomihisa Kamada, Satoru KawaiGraph Drawing:
Algorithms for the Visualization of Graphs
Stephen EickKenneth CoxRichard Becker (Visualizing Network
Data)
Tamara Munzner (H3viewer)John Lamping, Ramana Rao
(Focus+Context)George Furnas (Fisheye View)
Graph Drawing Survey Paper
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Data Visualization: AestheticsData Visualization &
Aesthetics:
Martin WattenbergDirector of Visual Communication Lab at IBM
Watson Center
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Data Visualization: AestheticsData Visualization &
Aesthetics:
Ben FryOrganic information design (Anemone)Software
visualization(Dismap)
Keywords: Qualitative versus quantitative representation of
data, algorithmic design, processing, genetic algorithms
Haplotypes
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Data Visualization: AestheticsData Visualization &
Aesthetics:
Golan LevinGolan Levin Home
Lisa JevbrattLisa Jevbratt Projects
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Data Visualization: MethodsData Visualization
Methods and AlgorithmsMDS, SOM,Force-Directed Placement,Grand
tour, Parallel Planes,Glyphs,Node and link displays, Tree maps,
Matrix representations, Cone trees, Fisheye views, Focus+context
views,Cartograms
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Data Visualization: AlgorithmsForce-Directed Placement
Proposed to achieve several aesthetic criteria about graph
layoutsUniform distribution of nodesUniform edge lengthsMinimum
edge crossingsSymmetrydemo
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Data Visualization: AlgorithmsForce-Directed Placement
Peter Eades proposed as a heuristic approach.The idea is to
calculate attractive forces between connected nodes and repulsive
forces between every pair of nodes. Force models varied
significantly:Eades: was complex to run in real timeFruchterman,
Reingold: reduced complexity of Eades equationsKamada Kawai: based
on Hooks law and minimization of energy
Iterative algorithms
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Data Visualization: AlgorithmsForce-Directed Placement
Ms thesis, Basak Alper
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Data Visualization: AlgorithmsMDS
Brief introA method for dimensionality reduction, enables to
visualize 40 dimensional data on a 2D displayThe idea is to keep
distance relations between nodes, proportionally consistent as you
reduce dimensions of the spaceIf distance in 40D space is d, then
distance in 2D space should be d , where is a constant for all
elements
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Data Visualization: AlgorithmsMDS
Multi-dimensional scalingMetric MDS methods based on eigen value
analysis of the matrix showing relatedness of every
elementNon-iterative and very costlyIf distance in 40D space is d,
then distance in 2D space should be d , where is a constant for all
elements
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Data Visualization: AlgorithmsMDS
Non-metric MDS is proposed by Kruskal to overcome problems with
metric MDSNon-metric MDS defines a stress function to place data
nodes on lower dimensional spaceNodes are displaced to lower stress
and iterations are stopped when overall stress reaches below a
certain thresholdStress function where d ij is the distance in high
dimensional space and g ij is the distance in low dimensional
spacedistance function is generally the Euclidian distance
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Data Visualization: AlgorithmsSOM
Kohonen self-organizing mapsAnother way of reducing dimensions
of data in a neural networks fashionPseudocode for the algorithm:
1. Initialize Map: Randomize the map's nodes' weight vectors 2.
Grab an input vector 3. Traverse each node in the map 1. Use
Euclidean distance to find similarity between the input vector and
the map's node's weight vector 2. Track the node that produces the
smallest distance 4. Update the nodes in the neighbourhood by
pulling them closer to the input vector (neighborhood function) 5.
Increment t and repeat while t < (total number of
iterations)
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Data Visualization: AlgorithmsSOM
Initialize map: Create a matrix of vectors, where the size of
these vectors is equal to the dimensions of data and magnitudes are
in the range of dataThe initial map can be totally random or
organized in a certain way, for instance magnitudesNeighborhood
function: is generally a Gaussian, and the radius is generally
reduced over iterations
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Data Visualization: AlgorithmsSOM
Resulting arrangement of the vectors on the map is based on
similarities of input data vectors.For each vector in higher
dimensions, the position of the closest vector on the map is its
position in lower dimensional space (which is generally 2D)
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Data Visualization: AlgorithmsSOM
Processing demoModel Tunes