Interactive and Dynamic Graphics for Data Analysis (eBook)

With R and GGobi

Dianne Cook, Deborah F. Swayne (Autoren)

eBook Download: PDF

2007 | 2007
XVIII, 188 Seiten
Springer New York (Verlag)
978-0-387-71762-3 (ISBN)

Infos

This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapter topics include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The book is augmented by a wealth of online material.

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This book is about using interactive and dynamic plots on a computer screen as part of data exploration and modeling, both alone and as a partner with static graphics and non-graphical computational methods. The area of int- active and dynamic data visualization emerged within statistics as part of research on exploratory data analysis in the late 1960s, and it remains an active subject of research today, as its use in practice continues to grow. It now makes substantial contributions within computer science as well, as part of the growing ?elds of information visualization and data mining, especially visual data mining. The material in this book includes: * An introduction to data visualization, explaining how it di?ers from other types of visualization. * Adescriptionofourtoolboxofinteractiveanddynamicgraphicalmethods. * An approach for exploring missing values in data. * An explanation of the use of these tools in cluster analysis and supervised classi?cation. * An overview of additional material available on the web. * A description of the data used in the analyses and exercises. The book's examples use the software R and GGobi. R (Ihaka & Gent- man 1996, RDevelopment CoreTeam2006) isafreesoftware environment for statistical computing and graphics; it is most often used from the command line, provides a wide variety of statistical methods, and includes high-quality staticgraphics.RaroseintheStatisticsDepartmentoftheUniversityofAu- land and is now developed and maintained by a global collaborative e?ort.

Inhalt

Preface 6
Contents 9
Technical Notes 12
List of Figures 14
1 Introduction 17
1.1 Data visualization: beyond the third dimension 17
1.2 Statistical data visualization: goals and history 19
1.3 Getting down to data 20
1.4 Getting real: process and caveats 24
1.5 Interactive investigation 31
2 The Toolbox 33
2.1 Introduction 33
2.2 Plot types 35
2.3 Plot manipulation and enhancement 51
2.4 Tools available elsewhere 60
2.5 Recap 61
Exercises 61
3 Missing Values 62
3.1 Background 63
3.2 Exploring missingness 64
3.3 Imputation 70
3.4 Recap 76
Exercises 77
4 Supervised Classification 78
4.1 Background 79
4.2 Purely graphics: getting a picture of the class structure 85
4.3 Numerical methods 92
4.4 Recap 114
Exercises 114
5 Cluster Analysis 117
5.1 Background 119
5.2 Purely graphics 121
5.3 Numerical methods 125
5.4 Characterizing clusters 139
5.5 Recap 140
Exercises 141
6 Miscellaneous Topics 143
6.1 Inference 143
6.2 Longitudinal data 148
6.3 Network data 153
6.4 Multidimensional scaling 159
Exercises 165
7 Datasets 167
7.1 Tips 167
7.2 Australian Crabs 168
7.3 Italian Olive Oils 169
7.4 Flea Beetles 171
7.5 PRIM7 171
7.6 Tropical Atmosphere- Ocean Array ( TAO) 173
7.7 Primary Biliary Cirrhosis ( PBC) 175
7.8 Spam 176
7.9 Wages 178
7.10 Rat Gene Expression 180
7.11 Arabidopsis Gene Expression 182
7.12 Music 185
7.13 Cluster Challenge 186
7.14 Adjacent Transposition Graph 186
7.15 Florentine Families 187
7.16 Morse Code Confusion Rates 188
7.17 Personal Social Network 189
References 190
Index 198

Produkt-Details

• Erscheint lt. Verlag: 5.9.2007
• Reihe/Serie: Use R!
• Reihe/Serie: Use R!
• Co-Autor: A. Buja, D. Temple Lang, H. Hofmann, H. Wickham, M. Lawrence
• Zusatzinfo: XVIII, 188 p.
• Verlagsort: New York
• Sprache: englisch
• Themenwelt: Informatik ► Datenbanken ► Data Warehouse / Data Mining
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Naturwissenschaften ► Biologie
• Themenwelt: Technik
• Schlagworte: classification • cluster analysis • Clustering • Data Analysis • data analysis software • Data Mining • Data Visualization • direct manipulation • multivariate data • Statistics • visual data mining
• ISBN-10: 0-387-71762-5 / 0387717625
• ISBN-13: 978-0-387-71762-3 / 9780387717623