内容

目次

Data Visualization: Introduction. Principles: A Brief History of Data Visualization.- Good Graphics?- Static Graphics.- Data Visualization Through Their Graph Representations.- Graph-Theoretic Graphics.- High Dimensional Data Visualization.- Multivariate Data Glyphs: Principles and Practice.- Linked Views for Visual Exploration.- Linked Data Views.- Visualizing Trees and Forests. Methodologies: Interactive Linked Micromap Plots for the Display of Geographically Referenced Statistical Data.-Grand Tours, Projection Pursuit Guided Tours and Manual Controls.- Multidimensional Scaling - A Review.- Huge Multidimensional Data Visualization: Back to the Virtue of Principal Coordinates and Dendrograms in the New Computer Age.- Multivariate Density Estimation for Visualization.- Structured Sets of Graphs.- Regression by Parts: Fitting Visually Interpretable Models with GUIDE.- Structural Adaptive Smoothing by Propagation-Separation-Methods.- Smoothing Techniques for Visualization.- Data Visualization via Kernel Machines.- Visualization of Cluster Analysis and Finite Mixture Models.- Visualizing Contingency Tables.- Mosaicplots and Their Variations.- Parallel Coordinates: Visualization and Classification of High Dimensional Datasets.- Matrix Visualization.- Visualization in Bayesian Analysis.- Java Tools and Environment for Statistical Data Visualization.- Web-Based Statistical Graphics Using XML Technologies. Selected Applications: Data Visualization for Genetic Networks Reconstruction.- Visualizing Genomic Data.- Reconstruction, Visualization, and Analysis of Medical Images.- Exploratory Graphics of a Financial Dataset.- Graphical Data Representation in Bankruptcy Analysis.- Visualizing Functional Data with an Application to eBay's Online Auctions.- Visualization Tools for Insurance Risk Processes.