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.
Interactive and Dynamic Graphics for Data Analysis
and over 1.5 million other books are available for Amazon Kindle . Learn more
| |||||||||||||||
Did you know you can trade in your old books for an Amazon.co.uk Gift Card to spend on the things you want? Visit the Books Trade-In Store for more details. Learn more. |
Book Description
Product details
Would you like to update product info or give feedback on images?
|
More About the Author
Discover books, learn about writers, and more.
Product Description
Review
From the reviews: "The central theme of the book is multivariate data visualization. The focus is on interactive and dynamic graphics.… The target audience of the book is (advanced) undergraduate and graduate students, but also industrial statisticians, engineers, bioinformaticists, and computer scientists.… Each chapter ends with some exercises. The chapters are well written and structured, and the examples are well documented.… A reader with a decent knowledge of multivariate statistics … will be happy with this book." (Ruud H. Koning, Kwantitatieve Methoden, 2008R14) "In this book, R commands are used to exploit the power of the GGobi systems for interactive and dynamic graphics.…This book, … is a very useful brief overview of the insight that a powerful modern suite of graphics tools may offer." (John Maindonald, International Statistical Review, 2008, 76, 3, pages 436-437) "Diane Cook and Deborah Swayne’s new book, part of Springer’s Use R! series, helps fill an important niche in the literature for the R community and readers of Biometrics. The title of the book indicates the substantial challenge: the book is neither purely methodological nor simply about software. It draws upon the expertise of a wide range of contributors.… I will borrow a phrase the authors use on the opening page: these resources help us "orient ourselves in the sea of information."" (Biometrics 2008) "The book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. … The book may be used as a text in a class on statistical graphics, exploratory data analysis, visual data mining, or information visualisation. It might also be used as an adjunct text in a course on multivariate data analysis or data mining. Moreover, the book is suitable for an industrial statistician, engineer, bioinformaticist, or computer scientist … . Finally, it may be useful to a mathematician … ." (Christina Diakaki, Zentralblatt MATH, Vol. 1154, 2009)
From the Back Cover
This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapters 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 role of graphical methods is shown at each step of the analysis, not only in the early exploratory phase, but in the later stages, too, when comparing and evaluating models. All examples are based on freely available software: GGobi for interactive graphics and R for static graphics, modeling, and programming. The printed book is augmented by a wealth of material on the web, encouraging readers follow the examples themselves. The web site has all the data and code necessary to reproduce the analyses in the book, along with movies demonstrating the examples. The book may be used as a text in a class on statistical graphics or exploratory data analysis, for example, or as a guide for the independent learner. Each chapter ends with a set of exercises. The authors are both Fellows of the American Statistical Association, past chairs of the Section on Statistical Graphics, and co-authors of the GGobi software. Dianne Cook is Professor of Statistics at Iowa State University. Deborah Swayne is a member of the Statistics Research Department at AT&T Labs.
Inside This Book
(Learn More)
What Other Items Do Customers Buy After Viewing This Item?
Customer Reviews
There are no customer reviews yet on Amazon.co.uk.
5 star
4 star
3 star
2 star
1 star
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
3.0 out of 5 stars
4 reviews
12 of 12 people found the following review helpful
4.0 out of 5 stars
A valuable addition to any data-miner's library which shows the power of dynamic graphical methods
27 May 2009
By Emre Sevinc
- Published on Amazon.com
Format:Paperback|Amazon Verified Purchase
This is definitely not an introductory tutorial or a how-to book on data visualization. The authors are the developers of the wonderful GGobi visualization system and its R integration and they have put lots of documentation online to which they don't hesitate to refer in many parts of the book. That means you have to have some background in R programming, statistical terminology, principal component analysis, etc.
The main strength of the book is providing very good examples depicting how dynamic graphics based analyses can help analytical methods. I especially consider the chapters on supervised classification and clustering very well designed. Many critical aspects are stressed and the importance of "looking at data before diving into support vector machines, linear discrminant analysis, decision trees and self-organizing maps" is shown from different perspectives (pun intended ;-)
If you are serious about data visualization, data mining and statistics then this book *along* with the accompanying website will be a very good guide. The exercises at the end of each chapter will also provide challenges as well as valuable insights.
The main strength of the book is providing very good examples depicting how dynamic graphics based analyses can help analytical methods. I especially consider the chapters on supervised classification and clustering very well designed. Many critical aspects are stressed and the importance of "looking at data before diving into support vector machines, linear discrminant analysis, decision trees and self-organizing maps" is shown from different perspectives (pun intended ;-)
If you are serious about data visualization, data mining and statistics then this book *along* with the accompanying website will be a very good guide. The exercises at the end of each chapter will also provide challenges as well as valuable insights.
5 of 5 people found the following review helpful
4.0 out of 5 stars
GGobi R development has stopped
21 Jan 2013
By Robert Williams
- Published on Amazon.com
Format:Paperback
This is a useful book and I plan to use it for teaching. However, as very useful as GGobi is, development has stagnated and it is no longer possible to use R to make nice looking graphs of GGobi results. The NSF support of the GGobi project ended, and the team dispersed. R packages needed to read GGobi graphs are obsolete and no longer available for the current version of R. This is a real pity. The only way to make publication plots of R GGobi objects now is to take png screen shots at just the right size so that when they are dropped into a manuscript they scale correctly, a very unsatisfactory solution. Still, R is a necessity for professional statisticians, GGobi is too useful to let die, and this book (together with the broken connection between GGobi and R with respect to graphics) continues to be useful.
3 of 3 people found the following review helpful
3.0 out of 5 stars
Does help you get into ggobi
15 Oct 2010
By Ekorn
- Published on Amazon.com
Format:Paperback|Amazon Verified Purchase
This book helped me get into ggobi, which is what I bought it for. Two main obstacles for new ggobi users are its peculiar user interface and awkward input of data. The latter issue is solved by the R package rggobi, which can use R data frames directly. The user interface is marginally better with rggobi; you can script the basic creation of figures, selection of datapoints, and choice of focal variables. The whole thing is still rather awkward, though: For example, to change views or interaction modes, you need to leave the figure window and visit the main ggobi window. While it is possible to change formatting (glyphs, colours, sizes) it is very difficult to figure out. The book is almost indispensable in taking you through the rather unintuitive workflow of (r)ggobi. The book also has OK introductions on classification and clustering. All in all it served its purpose, but I probably won't be coming back to it.
Users looking for an alternative might try the R package iplots, which has a somewhat smoother user interface; I ended up using both.
Users looking for an alternative might try the R package iplots, which has a somewhat smoother user interface; I ended up using both.
›
Go to Amazon.com to see all 4 reviews
3.0 out of 5 stars
Were these reviews helpful?
Let us know
Listmania!
Look for similar items by category
- Books > Computing & Internet > Digital Lifestyle > Online Shopping > Amazon
- Books > Computing & Internet > Software & Graphics
- Books > Reference
- Books > Science & Nature > Biological Sciences > Animal Sciences > Physiology
- Books > Science & Nature > Mathematics > Combinatorics & Graph Theory
- Books > Science & Nature > Mathematics > Probability & Statistics
- Books > Science & Nature > Popular Science > Maths
- Books > Scientific, Technical & Medical
Feedback
- Would you like to update product info or give feedback on images?
- I am the Author, and I want to comment on my book.
- I am the Publisher, and I want to comment on this book.
|
