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A Modern Theory of Factorial Design

that the design and implementation stages of statistical graphics play a very important role in dealing successfully with the challenges arising from large datasets. Chapter 4 concentrates on the application of interactive graphics to large datasets to show how they can help improve the power and potential of graphics in supplying more information by employing some sophisticated techniques such as querying, scaling, linking, zooming, and sorting. This chapter ends with some remarks about what should be done to improve interaction capabilities of existing data analysis tools when dealing with large and complex datasets. Chapters 5–11 that are on particular types of graphics for applications constitute the second part of the book titled as “Applications.” Chapter 5 examines the mosaic plots and their variations for multivariate categorical data involving both large numbers of cases and large numbers of categories. This chapter also demonstrates how valuable it is to use techniques like reordering and redmarking to produce clearer images of what is in the data. Chapter 6 concentrates on useful visualization techniques, known as rotating plots, that can be used to explore multivariate continuous data, and overviews the software developed and used for this purpose. Chapter 7 provides a very nice introduction to a smooth modified version of the parallel coordinate plot that uses color brushing to separate categories and can easily handle many observations in more than three dimensions. This chapter contains some mathematics to explain the logic behind the new modified parallel plot and shows by an example the role some sophisticated actions such as reordering and rescaling of axes can play in obtaining useful representations of large datasets. Chapter 8 examines the networks by concentrating on the difference between drawing optimal layouts and producing informative displays within a reasonable time limit. Because of focusing on the difference between time and quality, the emphasis in this chapter is on exploration and discovery that let the reader to reveal unusual features hidden within very large graphs by using a variety of layout algorithms. Chapter 9 examines trees that are known to be useful for exploring crucial patterns in the small datasets. This chapter generalizes the logic behind the trees to the large datasets by focusing on the task of combining and summarizing the information obtained from large numbers of trees complex datasets can produce. The behavior of some innovative visualization methods, such as fluctuation diagrams and treemaps, to get more information from large datasets is considered in detail in this chapter. Chapter 10 concentrates on some challenging visualization problems related to the “Mice and Elephants” graphic by applying it to the internet packet data. The methods offered in this chapter, such as biased sampling and quantile windows, to deal with the visualizations of large and complex datasets are shown to uncover some interesting structures in the internet traffic dataset. The last chapter, Chapter 11, puts the visualization methods described in the previous chapters of the book into action using a real dataset to illustrate how valuable visualization can be in the process of data analysis. This is a valuable book for all the researchers who need practical guidance to explore their large datasets by the help of a variety of visualization methods. Several applications discussed throughout the book and easy-to-read explanations of the methods make them easy to apply for anyone. We recommend this book for everyone interested in discovering structures hidden in the large datasets by a variety of state-of-the-art visualization techniques. Since the authors use the word “a million” throughout the book as a “useful symbolic target” for large datasets, we also think that it is going to be useful to read the third page of the book to learn what Francis Galton thought more than a century ago about what a million might look like.