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Graphics of Large Datasets: Visualizing a Million (Statistics and Computing)

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Graphics of Large Datasets: Visualizing a Million (Statistics and Computing)

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Name:Graphics of Large Datasets: Visualizing a Million (Statistics and Computing)

Infohash: 1A357C48A455EA197FBCDBD5F675E67226074355

Total Size: 30.90 MB

Seeds: 2

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Last Updated: 2011-01-01 22:57:12 (Update Now)

Torrent added: 2008-08-27 15:58:17






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Springer.Graphics.of.Large.Datasets.Jul.2006.pdf (Size: 30.90 MB) (Files: 1)

 Springer.Graphics.of.Large.Datasets.Jul.2006.pdf

30.90 MB
 

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Torrent description

Graphics are great for exploring data, but how can they be used for looking at the large datasets that are commonplace to-day? 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.

New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. There are considerable advantages in extending displays which are well-known and well-tried, both in understanding how best to make use of them in your work and in presenting results to others. It should also make the book readily accessible for readers who already have a little experience of drawing statistical graphics. All ideas are illustrated with displays from analyses of real datasets and the authors emphasize the importance of interpreting displays effectively. Graphics should be drawn to convey information and the book includes many insightful examples.

ISBN: 0387329064
Publisher: Springer
Date: 24 July, 2006
Author: Antony Unwin, Martin Theus, Heike Hofmann

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