Graphics of large datasets : visualizing a million /

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Bibliographic Details
Author / Creator:	Unwin, Antony.
Imprint:	New York : Springer, c2006.
Description:	1 online resource (xiii, 271 p.) : ill. (some col.)
Language:	English
Series:	Statistics and computing Statistics and computing.
Subject:	Statistics -- Graphic methods. Computer graphics. Computer graphics. Statistics -- Graphic methods.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/8877904

Hidden Bibliographic Details
Other authors / contributors:	Theus, Martin. Hofmann, Heike.
ISBN:	0387329064 9780387329062 9780387379777 0387379770 9786610938421 6610938423
Notes:	Includes bibliographical references (p. [251]-261) and indexes.
Summary:	"Graphics are great for exploring data, but how can they be used for looking at the large datasets that are commonplace today? This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases or large in numbers of variables or large in both. Data visualization is useful for data cleaning, exploring data, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis and data mining. Data analysts, statisticians, computer scientists - indeed anyone who has to explore a large dataset of their own - should benefit from reading this book."--Jacket.
Other form:	Print version: Unwin, Antony. Graphics of large datasets. New York : Springer, c2006 0387329064 9780387329062

Table of Contents:

Cover
Contents
1 Introduction
1.1 Introduction
1.2 Data Visualization
1.3 Research Literature
1.4 How Large Is a Large Dataset?
1.5 The Effects of Largeness
1.5.1 Storage
1.5.2 Quality
1.5.3 Complexity
1.5.4 Speed
1.5.5 Analyses
1.5.6 Displays
1.5.7 Graphical Formats
1.6 What Is in This Book
1.7 Software
1.8 What Is on the Website
1.8.1 Files and Code for Figures
1.8.2 Links to Software
1.8.3 Datasets
1.9 Contributing Authors
Part I: Basics
2 Statistical Graphics
2.1 Introduction
2.2 Plots for Categorical Data
2.3 Plots for Continuous Data
2.4 Data on Mixed Scales
2.5 Maps
2.6 Contour Plots and Image Maps
2.7 Time Series Plots
2.8 Structure Plots
3 Scaling Up Graphics
3.1 Introduction
3.2 Upscaling as a General Problem in Statistics
3.3 Area Plots
3.4 Point Plots
3.5 From Areas to Points and Back
3.6 Modifying Plots
3.7 Summary
4 Interacting with Graphics
4.1 Introduction
4.2 Interaction
4.3 Interaction and Data Displays
4.4 Interaction and Large Datasets
4.5 New Interactive Tasks
4.6 Summary and Future Directions
Part II: Applications
5 Multivariate Categorical Data
Mosaic Plots
5.1 Introduction
5.2 Area-based Displays
5.3 Displays and Techniques in One Dimension
5.4 Mosaic Plots
5.5 Summary
6 Rotating Plots
6.1 Introduction
6.2 Beginning to Work with a Million Cases
6.3 Software System
6.4 Application
6.5 Current and Future Developments
7 Multivariate Continuous Data
Parallel Coordinates
7.1 Introduction
7.2 Interpolations and Inner Products
7.3 Generalized Parallel Coordinate Geometry
7.4 A New Family of Smooth Plots
7.5 Examples
7.6 Detecting Second-Order Structures
7.7 Summary
8 Networks
8.1 Introduction
8.2 Layout Algorithms
8.3 Interactivity
8.4 NicheWorks
8.5 Example: International Calling Fraud
8.6 Languages for Description and Layouts
8.7 Summary
9 Trees
9.1 Introduction
9.2 Growing Trees for Large Datasets
9.3 Visualization of Large Trees
9.4 Forests for Large Datasets
9.5 Summary
10 Transactions
10.1 Introduction and Background
10.2 Mice and Elephant Plots and Random Sampling
10.3 Biased Sampling
10.4 Quantile Window Sampling
10.5 Commonality of Flow Rates
11 Graphics of a Large Dataset
11.1 Introduction
11.2 QuickStart Guide Data Visualization for Large Datasets
11.3 Visualizing the InfoVis 2005 Contest Dataset
References
Authors
Index
Last Page.

Graphics of large datasets : visualizing a million /

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