Subject Index

α-blending, ,  anomaly, ,  D plot, , anti-Robinson,  approximate degrees of freedom, ,, , Abbott, Edwin,  , ,  acceleration,  arithmetic accuracy ratio,  − political, ,  active contour model,  ART, see algebraic reconstruction technique active plot,  aspect ratio, ,  acyclicgraph,, association plot, ,  adaptive smoothing, ,  association rules,  adaptive weights smoothing,  atlas additive models, – − cartographic,  additive tree,  − statistical, ,  adjunct variables,  atomic number,  adverse events, – attribute operations,  age pyramid,  attributes,  aggregate claim process,  auction, ,  aggregated view,  auction profiles,  aggregated views, linking,  averagelinkage,, AIC, see Akaike’s information criterion averaged shifted histogram, ,  Akaike’s information criterion, , , , axis variables,   algebraic linking,  B-spline,  algebraic reconstruction technique,  backfitting algorithm, ,  alignment,  background variable,  analyse des données,  bagging,  analysis Baker, Robert,  − discriminant,  balloon plot,  − principal component,  bandwidth, , , , ,  analysis of covariance, ,  − smoothing parameter,  Anderberg,  − variable,  angular separation,  bankruptcy analysis,  922 Subject Index barchart,,,,,, brush mode,  − divided,  brushing, , , ,  Barbeu-Dubourg, Jacques,  Buache, Philippe,  barplot,  Bundesbank, , , ,  baseball data,  Bayesian data analysis,  C (programming language), ,  Bayesian estimate,  Caesarean data set,  Bayesian exploratory data analysis,  calendar plots,  Bayesian information criterion,  calendar time,  Bayesian networks,  calendar-based,  Bayesian statistics,  camera obscura,  Bell Laboratories,  Canberra metric,  Benzécri, Jean-Paul, ,  cancer atlas,  Bertin plot,  canonical correlation analysis, , ,  Bertin, Jacques, ,  − kernel,,,– Bhattacharya distance,  − reduced kernel,  bias,,,– caption,  − grouping-based,  cars data set, ,  − perception-based,  CART, , , , ,  − proximity-based,  cartesian product, –, , , , , bias-optimized frequency polygon,  ,  BIC, see Bayesianinformation criterion cartesian space, – bid histories,  case-by-variables matrices,  bid-arrival,  catastrophic event,  bidding data, ,  categorical data,  bidirectional color,  CCmaps,  bidirectional linking,  cDNA,,, bilateral frequency polygon,  cell-cycle,  bills of mortality,  censored zooming,  binary data, ,  centroid,  binary response model, ,  centroid-based cluster analysis, see cluster anal- binwidth,  ysis biplot,  chart bivariate normal surface,  − bar,,,, block clustering,  − control,  Boolean networks,  − Gantt,  bootstrap, , –,  − isochronic,  Boston housing,  − pie,  Bowley, Arthur, ,  − time-series,  Box–Cox transformation,  Cheysson,Émile,, boxplot, , , , –, , , , chi-square distance,  , ,  chi-squared test,  Brahe, Tycho, –,  choropleth,  Braun, Blanque,  choropleth maps,  Bray–Curtis,  city block metric,  Subject Index 923 claim arrival process,  comma separated value (CSV) format,  class diagram,  command line, , ,  classical scaling,  common scale,  classification, ,  common scaling, ,  classification in data, – company rating,  classification tree,  complete graph,  cluster analysis, , –, –, , compositing operator,   composition,  − centroid-based,  Comprehensive R Archive Network,  − computational issues,  computational − furthest neighbor,  − complexity,  − hierarchical, , , ,  −− clustering,  − mixed strategy, ,  − time,  −− large datasets,  concordance measures,  − model-based,  concurrency, , ,  − nearest neighbor,  conditional correlation,  − nested partitions,  conditional independence, ,  − nonhierarchical,  conditional inference,  − partitioning, , ,  conditioned choropleth maps,  −− large dataset,  conditioning variables,  − single linkage,  confidence bounds, ,  − tree, see dendrogram confidence curves,  −− alternative views,  confidence region,  −− structure,  consistency, ,  cluster tree,  contingencytable,, cluster views linked to statistical charts,  continuous distribution function,  clustering,,,,,, contour plot, , , , , ,  collinearity,  − trivariate,  color choice, , , , , , ,  contour shell,  color histogram,  convex hull, ,  color images, ,  Cook’s distance,  color palette coordinate − diverging,  − parallel,  − qualitative,  coordinate paper,  color projection,  coordinate system, , , ,  color space coplot,  − CIELAB,  correlation,,,,,– − CIELUV,  − marginal,  − HCL,  correlation matrix, ,  − HSV,  correspondence analysis, ,  − RGB,  corrgrams,  color spectrum, ,  cost function, – Colorbrewer, ,  counting process,  colour,  coupling,  colour coding, ,  covariance matrix, ,  924 Subject Index covariance operator,  −− high dimensional,  covariate adjustment,  data set coxcomb,  − alpha, –, , – CRAN, see Comprehensive R Archive Network − Caesarean,  crash tests,  − cars,, creation of functional observations,  − cdc, – critical path method,  − cdc, – Crome, August F.W.,  − dentitio,  cross-sectional, ,  − detergent,  cross-validation, , , , , ,  − elu, ,  cubic smoothing spline,  − German election,  curse of dimensionality,  − Pollen,  curve,  − Tour de France,  − contour,  de Fourcroy, Charles,  − cumulative frequency,  de Witt, Jan,  − fitting,  deceleration,  − freehand,  decision support, – − frequency,  decision tree,  − historical,  deductible,  − isodic,  degree of interest,  − isogen,  Delaunay triangulation,  − loess,  dendrogram,,,,, − selection,  − cutting level,  Cusa, Nicolas of,  density estimation,  cut point,  density evolution,  cylindrical probability,  dentitio data,  Czekanowski, Sørensen, Dice,  dependency inversion principle,  depth shading,  DAG, see directed acyclic graph derivative,,,, data Descartes, René, ,  − analysis,  design patterns, ,  − arthritis,  − decorator pattern,  − Danish corporal punishment,  − factory method pattern,  − display,  − mediator pattern,  − financial, – − observer pattern,  − fit,  − state pattern,  − GIS, – − strategy pattern,  − hospital,  details,  − multivariate,  detergent data,  − scaling,  DFBETAS, ,  − smoothing,  dffits,  − sphering,  diagnostic, ,  − transformation,  diagram − UCB admissions,  − alignment,  − visualization, , , ,  − butterfly,  Subject Index 925

− cartogram, ,  distribution − Chernoff faces,  − Burr, ,  − circle,  − exponential, ,  − contour, ,  − gamma,, − coxcomb,  − log-normal, ,  − divided circle,  − loss,  − geometric,  − mixture of  exponentials, ,  − Hertzsprung–Russell,  − Pareto, ,  − historical,  − tail,  − nomogram,  − Weibull, ,  − pie,  divergence metric,  − rose, ,  domain-specific linking,  − sieve,  domain-specific visualizations,  − statistical,  dotplot, ,  − stereogram,  doubledecker plot, , ,  − tree,  drill-down, ,  differential equation, ,  du Carla-Boniface, Marcellin,  dimension dualities − high,  − -D,  − reduction, , , , ,  Dupin, Charles,  − three,  dynamic binding,  dimension ordering,  dynamic interaction,  − correlation-driven,  dynamic sliced inverse regression (DSIR),  − data-driven,  dynamics,  − symmetry-driven,  − user-driven,  e-learning, ,  dimensionality,,, eBay, , , ,  direct clustering,  EDA, see exploratory data analysis directed acyclic graph,  edge frequency polygon,  discretization, ,  edge-preserving smoothing,  disparity,  effect display − curvature,  − fourfold,  − interaction,  − trellis,  effect-ordered data display, ,  − two-way table,  Eigenvalue,  dissimilarity,  − decomposition,  dissimilarity measure,  Eigenvector,  distance,  elliptical glyphs,  − in Rp, elliptical seriation, , , ,  distance measure,  EM algorithm,  − Euclidean,  empirical linking, ,  − Manhattan,  energy data,  distance transfer function,  entropy,  distance-based linking,  equally spaced grid,  distortion, ,  Euclid,  926 Subject Index

− “elements”,  forecast,,, Euclidean distance, , ,  forest, ,  Euclidean geometry, ,  FORTRAN,  evaluation, ,  frequency polygon,  − perception-based,  − bias-optimized,  − performance-based,  − edge,  event-driven programming,  Friendly, Michael,  evolution,  functional,  expected shortfall,  − data, ,  exploration, ,  − model,  exploratory data analysis, , , , , − object,,, , ,  − observation,  − atomic queries, – − summary,  − compound queries, – functional data analysis,  − guidelines, – fuzzy logic algorithm,  − requirements,  − visual cues,  Gabor filter,  exploratory graphics,  Galilei, Galileo,  Galton,Francis,,,– faceting,  Gannett, Henry,  factor analysis, ,  GAP, see generalized association plots − factorial plan,  Gauss, Carl Friedrich,  − output interpretation,  Gaussian distribution,  − visualization,  Gemma-Frisius, Reginer,  FDA, see functional data analysis general similarity coefficient,  feature map, –, ,  generalized association plots, , ,  feature space,  genetic algorithms,  Fermat, Pierre,  German Bundesbank,  figure German election data,  − geometric,  GGobi, , , , ,  filtered kernel,  Giffen, Robert,  filtered mode tree,  Gini index,  filtering,, GIS,  − curves,  GIS data, – final auction price,  glyph, , ,  financial data, – − design,,,, finite mixture model,  − evaluation,  finite time horizon,  − examples, ,  flexclust,  − limitations,  flexmix,  − strengths,  flipping of intermediate nodes,  gold & currencies, – flow map, ,  Golden Age, ,  fluctuation diagram, , ,  Goodman, Leo,  focusing,  goodness-of-fit,  fonts,  gradient fill,  Subject Index 927

Grammar of Graphics,  − model,  grandtour,,,, −− parameter naming,  graph, , , ,  −− sources of variation,  − age pyramid,  − tree,  − bilateral polygon,  − view,  − circle,  hierarchical cluster analysis, see cluster anal- − hanging rootogram,  ysis − high-resolution,  high-dimensional data visualization,  − line, ,  high-dimensional visualizations, interacting − model,  with,  − paper,  higher-order kernel,  − time-series,,,, highlighting, ,  graphical − linked,  − Gaussian model,  histogram,,–,,,,,, − interface,   − layout,  − averaged shifted, ,  − parameter,  − bivariate, – − path,  − density,  − primitive, ,  − frequency,  − test statistic,  − percentage,  graphics histospline,  − multivariate,  HLS, , ,  Graunt, John, ,  Homals,  Greenacre, Michael,  homogeneous Poisson process (HPP),  Gresham’s Law,  HTML,  grid Human–Computer Interaction Laboratory, − coordinate,   − hexagonal,  hundreds of variables, – group stimulus space,  Huygens, Christiaan,  Guerry, André-Michel, ,  GUI, , ,  icon, see glyph GUIDE,  identity linking,  image Haberman, Shelly,  − analysis,  Halley, Edmund,  − fusion,  Hamman,  − plot,  Harrison, John,  − reconstruction,  head injury,  − segmentation,  heatmap,  impurity measure,  Herschel, William,  independence,  hexagonal bin, ,  INDSCAL,  Hidalgo stamps,  information loss,  hierarchical inheritance,  − clustering, , ,  insurance,  − linking,  − automobile,  928 Subject Index

− company kernel machine, ,  −− bankruptcy,  − support vector clustering,  − premium,  − kernel canonical correlation analysis, , − relative safety loading,  , – insurer’s surplus,  − kernel principal component analysis, , integrating data –,  − Dynamic Signal Maps,  − smooth support vector machine,  − KnowledgeEditor,  − smooth support vector regression,  − PathFinder,  − support vector clustering, , , – − Pathway Assist,   − PubGene Vector PathBlazer,  kernel method,  interactive, , , , , ,  keyword data,  − graphics, , , , ,  knots,  − operation,  Kulczynski,  − plot, – − statistical graphic,  lack-of-fit, ,  − visualization, ,  Lallemand, Charles,  interpolation, , ,  Lambert, Johann,  interquartile range,  Laplace, Pierre Simon,  inverse problem,  large data sets,  isoline,  larger view,  isomap,  lassoing,  Italian Household Income and Wealth,  lattice graphics,  layer, , ,  Jaccard coefficient, ,  layout,  Jasp,  − data-driven,  Jasplot, ,  − manager,  , , , ,  − structure-driven,  − Abstract Window Toolkit (AWT),  le Blon, Jacob,  − Java D, ,  leaf,  − Java Foundation Classes (JFC),  legend, ,  − Swing,  Levasseur, Pierre Émile,  Jevons, William Stanley,  leverage,  JPEG,  life table,  juxtaposition,  likelihood ratio test,  limited expected value function,  k-means, ,  line Karush–Kuhn–Tucker conditions,  − contour,  KEGG,  − graph Kendall’s tau coefficient,  −− -D,  kernel,  − isogon,  kernel density estimate, –,  − isoline, ,  − bivariate, – − slope as rate,  − multivariate, – − timeline,  kernel function,  − type, ,  Subject Index 929 linear − geological,  − graph,  − linked to statistical charts,  − projection,  − shaded,  linear regression − thematic, ,  − multiple,  − topographic,  linkage methods,  − weather, ,  linked mappings,  − brushing, ,  − many-to-one,  − clustering view,  − one-to-many,  − data view,  − one-to-one,  − highlighting, , ,  Marey, Étienne Jules,  − map,  marginal panels,  − micromap plot,  marketplaces,  − network graph,  Markov chain Monte Carlo (MCMC),  − plot, ,  Markov property,  − view,,,,, Marshall, Alfred,  linking,  mathematical framework,  − with memory,  matrix − without memory,  − scatterplot,  Liskov substitution principle,  matrix maps, ,  Lisp-Stat, ,  matrix visualization, , , , ,  lithography,  Maunder, E.W.,  LM plots,  maximum likelihood estimate (MLE),  local linear, , , , ,  mean loglinear model,  − acceleration,  longitude,  − excess function, ,  loss distribution,  − integrated squared error,  loss of information,  − residual life function,  low-rank approximation,  − squared error,  lowess,  − velocity,  measurement MacSpin,  − physical,  magnetic resonance images (MRI),  measurement error,  Mahalanobis distance,  medical image,  majorization, ,  metabolic networks,  MANET, ,  metric, see distance Manhattan distance,  metric scaling, ,  map,,,, metric space,  − anamorphic,  Michael,  − cartography,  microarray data, , , , , ,  − chloropleth,  microarrays,  − contour,  micromaps, ,  − disease,  microplot, ,  − epidemiological,  Microsoft Excel format,  − flow, ,  Minard,CharlesJoseph,,, 930 Subject Index minimal span loss,  mountainplot,, Minkowski metric,  Mountford,  missing functional objects,  multidimensional scaling, , , ,  missingvalues,,,, multilevel models,  mode multiple correspondence analysis,  − forest,  multiple linked views,  − surface,  multiple testing,  − tree,  multiscale visualization, – −− filtered,  multivariate data,  model multivariate Gaussian distribution,  − checking, ,  multivariate graphics,  − generalized linear,  mussels − loglinear,  − horse,  − misspecification,  − mixed,  Nadaraya–Watson,  − piecewise constant,  neighborhood graph,  − piecewise linear,  network graphs linked to statistical charts,  − selection,  neural networks,  − tree-structured,  Neurath,  − understanding,  NHTSA,  model–view–controller (MVC), ,  Nightingale, Florence,  model-based cluster analysis, see cluster anal- node,  ysis noisy realizations,  modeling and simulation nominal data,  − GenePath,  nominal variable,  − Genetic Network Analyzer,  nomogram,  Mondrian, ,  non-metric scaling, ,  monitor,  nonlinear color mapping,  monolithic visualizations,  nonlinear models, – monotone smoothing,  nonparametric density estimation,  mosaic nonparametric regression,  − mondrian display,  normalization,  − multiple bars,  − reorder rows and columns,  object,  − same-bin-size display,  object-oriented programming (OOP),  mosaicplot,,,,,,,,, observed bids,  , , , , , ,  occlusion, ,  − conditional probability,  Ochiai,  − construction,  odds ratio − fluctuation diagram,  − conditional,  − multiple barcharts,  − sufficient set,  − order of variables,  − definition,  − same bin size,  − high dimensional generalization,  − variations,  online,  Moseley, Henry,  online auction, ,  Subject Index 931 open-closed principle,  particle swarm optimization,  OpenGL,  partition operational taxonomy units,  − recursive,  ordering,  partitioning around medoids,  Oresme, Nicole,  partitioning cluster analysis, see cluster anal- Ortelius, Abraham,  ysis outlier, , , , , ,  Pascal, Blaise,  outlier detection,  passive plot,  overall fitting,  Patefield algorithm,  overlay,  path,  overview,  pathway, ,  pattern, ,  p-values, ,  − fill,  paint mode,  − recognition, , ,  pairs plot,  − search,  PAM, see partitioning around medoids PCA, see principal component analysis panel plot,  PDF,  parallax software,  Pearson correlation, ,  parallel coordinate, , –,  Pearson residuals,  − axes permutations, – Pearson, Karl,  − classifiers, – penalized smoothing spline,  − D’Ocagne,  penalized squared error,  − decision support, – penalizing term,  − dualities penalty term,  −− -D,  permutation, , ,  − exploration guidelines, – permutation matrices,  − exploratory data analysis, – permutation test,  − origins, – Perozzo,Luigi, − Parallax software,  perspective plot, ,  − plot, , , , , , , , , Petermann, Augustus,  , ,  Petty, William, ,  − projective plane,  phase-plane plots, ,  − proximate planes,  Phi,  − queries pictogram,  −− atomic,  pie chart, ,  −− atomic – EDA, – piecewise additive,  −− compound boolean – EDA, – piecewise constant,  −− compound-boolean,  piecewise linear,  −− exploration,  piechart,  − relations patterns,, Pima Indian data,  − review, – pivot table,  − scatterplot matrix,  planar graph,  − visual cues,  Playfair,William,,,,,, parametric bootstrap,  plot partial functional objects,  − association,  932 Subject Index

− bivariate,  −− points,  − Fourier function,  −− variables,  − log-log, ,  principal components,  − mosaic, , ,  printing − parallel coordinate,  − three-colour,  − probability, ,  probability − star, ,  − cylindrical,  − stem-leaf,  − gate,  PNG,  − plot,  pointwise histograms,  − product,  Poissonregression,, probability theory,  political arithmetic,  process politician data,  − aggregate claim,  Pollen data set,  − claim arrival,  polymorphism,  − counting,  polynomial spline,  − density evolution,  positive definite kernel, ,  − Poisson positron emission tomography (PET),  −− homogeneous (HPP),  posterior predictive checking,  − renewal,  posterior predictive distribution,  − risk,  posterior uncertainty,  −− trajectory,  PostScript,,,,,,, − stationary,  power law,  Procrustes analysis, ,  prediction shading,  product kernel, , – predictive distribution, ,  product probability,  preprocessing, ,  profile plots,  presentation,  profiles,  presentation graphics,  projection, ,  price − axonometric,  − curve, , ,  projection pursuit, ,  − dynamic, ,  projection pursuit index,  − evolution,  projection pursuit, central mass index,  Priestley, Joseph, , ,  projection pursuit, holes index,  PRIM-,  projection pursuit, LDA index,  primary monotone least-squares regression, projection pursuit, optimization,   projection pursuit, PCA index,  principal component analysis, , , , propagate,  , , , , , ,  propagationcondition,,,,, − correlation circle,  Propagation-Separation (PS),  − kernel, , –,  Property Claim Services,  − principal components,  proximate planes,  −− correlations,  proximity data,  −− orthogonality,  proximity graph,  − reduced kernel,  proximity matrices, ,  − supplementary Psychometrika,  Subject Index 933

Ptolemy, Claudius, ,  renewal process,  Python,  reorderable matrix,  reordering,  Quételet, Adolphe, , ,  replacement,  quantile,  replicateddata,, quantile line,  replication,  query replication distribution,  − atomic,  reproducing kernel,  −− explained, – reproducing kernel Hilbert space, ,  − atomic – EDA, – residual,  − compound boolean – EDA,  residual-based shading,  − compound-boolean,  resolution of a statistical graph,  − compound-boolean – EDA,  response model,  − exploration,  response surface, – RGB R,,,,,,,,,, − cube,  R, ,  Rheticus, Georg,  RWinBUGS, ,  Rice Virtual Lab in Statistics,  radial basis function, , ,  ridge regression rail travel data,  − smooth support vector regression,  random forest,  risk process,  random graph,  − α-stable Lévy motion approximation,  random subset,  − diffusion approximation,  Rao,  − trajectory,  rating,  reciprocal averaging,  Riverplot,  recover,  Robinson matrix,  rectangle selection,  ROC curve,  − recursive partitioning,  area under,  reduced kernel, , , ,  Rogers, Tanimoto,  reference band, , , ,  rooted tree,  reference distribution, ,  rootogram, ,  reference region,  roughness functional,  region competition,  roughness penalty,  regression, , , ,  Royal Statistical Society, ,  − diagnostic,,, rubber-band selection,  − model,  rug fringe,  − Poisson, ,  rug plot, ,  − sliced inverse,  ruin − stepwise,  − probability,  − tree, , ,  − theory,  relationship, ,  Ruin Probabilities toolbox,  relative risk, – ruin probability,  relative safety loading,  ruin theory,  relativity of a statistical graph, , ,  Russell,  934 Subject Index

S-Plus, , ,  significance probability,  Saccharomyces cerevisiae yeast,  silhouette plot,  Sammon map,  silhouette value,  SAS,  similar set,  scagnostics,  similarity,  scalability, ,  similarity coefficient,  , , , , , , similarity metric,  ,  simple effects, , ,  scale,  simple matching coefficient, ,  − common,  Simpson,  scaling,  simulated annealing,  − multidimensional, ,  SiZer plot,  scattergram,  sliced inverse regression (SIR),  scatterplot, –, , , , , , small multiple, , ,  , ,  Smalltalk,  − brushing, ,  Smith, William,  − matrix, , , , , , –, , smoother,  , , , , , , ,  smoothing, , ,  Scheiner, Christopher,  smoothing parameter, , , –, , Schwabe, Hermann,  , , ,  scree plot,  smoothing parameters,  SDE-Solver,  smoothness,  SearchBox,  snake energy model,  searching,  snake–balloon model,  sectional display,  Sneath, Peter H.A.,  sectioned scatterplot, , ,  Snow, John, ,  sediment display,  Soergel metric,  selecting,  software,  selection bias,  − Ruin Probabilities toolbox,  selection modes,  − SDE-Solver,  self-organizing map, ,  − XploRe semitransparency, ,  −− Insurance Library,  Senefelder, Aloys,  Sokal, Robert,  separation,  Solka, Jeff L.,  sequences,  SOM, see self-organizing map seriation,  sorting, ,  shadow value, ,  Sorting auctions,  Shakespeare keywords,  spanning tree,  shape,  spatial data,  Shepard plot,  Spearman’s rank correlation,  shingles,  speckle noise,  shortest path,  spectrum,  shortest spanning path,  spineplot, ,  sieve diagram,  spineplot of leaves, ,  sieve plot, ,  spline order,  Subject Index 935 split,  tableau-graphique,  SPLOM, see scatterplot matrix tabular,  SPOL, see spineplot of leaves tabular views,  spreadplot,  tandem analysis,  spring model,  Tcl/Tk,  star plot, ,  temporal information,  static,  terminalnode,,,, static graphs,  test statistic,  statistical atlas,  thematic cartography,  statistics theorem − demographic,  − Ekart and Young,  − moral,  three-dimensional scatterplot,  stem-leaf plot,  threshold, ,  stereogram,  time boxes,  Steven and Weber laws,  timeseries,,,,, stratigraphic geology,  − multiple,  streaming,  − operation,  stress, ,  time-lagged, – strip labels,  TimeBox, ,  structural adaptation,  timeline, ,  Sturges’ rule,  TimeSearcher,  subjects space,  Titanic,  sufficient matrix visualization,  tour summary,  − axes,  summary statistics, ,  − basis,  supervised classification,  − finding structure, ,  supervised classification, LDA,  − frame,  supervised classification, QDA,  − geodesic,  supervised classification, trees,  − grand,,, support vector, ,  − guided, ,  − bounded,  − interpolation,  support vector machine,  − manual, ,  − smooth,  − path,, support vector regression − target basis,  − smooth,  − target plane, ,  surface estimation, –, ,  − within-plane,, SVG, see scalable vector graphics Tour de France, ,  SVM, see support vector machine − data set,  symbol trace plot, ,  − sunflower,  transformations,  traveling salesman, ,  table tree seriation,  − graphical,  treemap,,,,, − life,  Trellis − semi-graphic,  − display,,,, 936 Subject Index

− graphic,  − Graphviz,, − layout,  − interaction effects,  − paradigm, –, ,  − multidimensional,  − plot,,,, − Ospray,  trends,  − system,  triangulation,  − the case for, – Tufte, Edward, , ,  VITAMIN-S, VIsualization and daTA MIN- Tukey, John Wilder, , ,  ing System,  two-way table,  von Mayr, Georg,  typesetting,  Voronoi partition,  Voronoi tessellation,  ultrametric tree,  ultrasound image,  Walker,FrancisA., unaggregated view,  wall quadrant,  unidimensional scaling,  Wave–Hedges,  unified modeling language (UML),  weather map, ,  ,  Wegman, Edward J.,  unrepresentative,  weighted Euclidean distance,  weighted graph,  weighted least square estimate (WLSE),  van Langren, Michael F.,  weighted plots, ,  variability band, , , ,  widgets,  variable bandwidth,  wireframe plot,  variable location,  WMF,  variable selection, , ,  World Wide Web,  vertex, ,  view box,  XD,  viewport,  XGobi, ,  virus data,  XML,  vision model,  XploRe ViSta,  − Insurance Library,  visual thinking,  xysplom, , ,  visualization, , , , , ,  − Archimedes,  yeast two-hybrid screen,  − BioMiner,  Yule,  − displays for exploration,  − early successes,  Zeuner, Gustav,  − Geometry,  zooming, , 