Handbook of Data Visualization (Springer Handbooks of Computational Statistics)
| Date: 06 May 2011, 20:09
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Note: With 569 Figures and 50 Tables.
Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This new volume in the series Springer Handbooks of Computational Statistics gives an overview of modern data visualization methods, both in theory and practice. There are definitive chapters on modern graphical tools such as mosaic plots, parallel coordinate plots and linked views. There are chapters dedicated to graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well as chapters on software for graphics. Specialists from all over the world have contributed papers on their areas of expertise.
Table of Contents
I. Data Visualization
I.1 Introduction
II. Principles
II.1 A Brief History of Data Visualization
II.2 Good Graphics?
II.3 Static Graphics
II.4 Data Visualization Through Their Graph Representations
II.5 Graph-theoretic Graphics
II.6 High-dimensional Data Visualization
II.7 Multivariate Data Glyphs: Principles and Practice
II.8 Linked Views for Visual Exploration
II.9 Linked Data Views
II.10 Visualizing Trees and Forests
III. Methodologies
III.1 Interactive Linked Micromap Plots for the Display of Geographically Referenced Statistical Data
III.2 Grand Tours, Projection Pursuit Guided Tours, and Manual Controls
III.3 Multidimensional Scaling
III.4 Huge Multidimensional Data Visualization: Back to the Virtue of Principal Coordinates and Dendrograms in the New Computer Age
III.5 Multivariate Visualization by Density Estimation
III.6 Structured Sets of Graphs
III.7 Regression by Parts: Fitting Visually InterpretableModels with GUIDE
III.8 Structural Adaptive Smoothing by Propagation-Separation Methods
III.9 Smoothing Techniques for Visualisation
III.10 Data Visualization via Kernel Machines
III.11 Visualizing Cluster Analysis and Finite Mixture Models
III.12 Visualizing Contingency Tables
III.13 Mosaic Plots and Their Variants
III.14 Parallel Coordinates: Visualization, Exploration and Classiication of High-Dimensional Data
III.15 Matrix Visualization
III.16 Visualization in Bayesian Data Analysis
III.17 Programming Statistical Data Visualization in the Java Language
III.18 Web-Based Statistical Graphics using XML Technologies
IV. Selected Applications
IV.1 Visualization for Genetic Network Reconstruction
IV.2 Reconstruction, Visualization and Analysis of Medical Images
IV.3 Exploratory Graphics of a Financial Dataset
IV.4 Graphical Data Representation in Bankruptcy Analysis
IV.5 Visualizing Functional Data with an Application to eBay's Online Auctions
IV.6 Visualization Tools for Insurance Risk Processes
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