内容

目次

Data Visualization: Introduction. Principles: A Brief History of DataVisualization.- Good Graphics?- Static Graphics.- Data Visualization ThroughTheir Graph Representations.- Graph-Theoretic Graphics.- High DimensionalData Visualization.- Multivariate Data Glyphs: Principles and Practice.-Linked Views for Visual Exploration.- Linked Data Views.- Visualizing Treesand Forests. Methodologies: Interactive Linked Micromap Plots for the Displayof Geographically Referenced Statistical Data.-Grand Tours, ProjectionPursuit Guided Tours and Manual Controls.- Multidimensional Scaling - AReview.- Huge Multidimensional Data Visualization: Back to the Virtue ofPrincipal Coordinates and Dendrograms in the New Computer Age.- MultivariateDensity Estimation for Visualization.- Structured Sets of Graphs.- Regressionby Parts: Fitting Visually Interpretable Models with GUIDE.- StructuralAdaptive Smoothing by Propagation-Separation-Methods.- Smoothing Techniquesfor Visualization.- Data Visualization via Kernel Machines.- Visualization ofCluster Analysis and Finite Mixture Models.- Visualizing Contingency Tables.-Mosaicplots and Their Variations.- Parallel Coordinates: Visualization andClassification of High Dimensional Datasets.- Matrix Visualization.-Visualization in Bayesian Analysis.- Java Tools and Environment forStatistical Data Visualization.- Web-Based Statistical Graphics Using XMLTechnologies. Selected Applications: Data Visualization for Genetic NetworksReconstruction.- Visualizing Genomic Data.- Reconstruction, Visualization,and Analysis of Medical Images.- Exploratory Graphics of a FinancialDataset.- Graphical Data Representation in Bankruptcy Analysis.- VisualizingFunctional Data with an Application to eBay's Online Auctions.- VisualizationTools for Insurance Risk Processes.