Picturing high dimensions

This chapter has an explanation about what it means to have a high dimensional space, and how to look at high dimensional data using low dimensional linear projections delivered by a tour. It starts with an intuitive description of tours, and follows with some details of how to read axes, and how to detect different types of patterns, such as clustering and outliers. There is a section on history of the methods and interactive graphics, and an example that illustrates what can be learned that would be missed without using these methods. An additional section overviews the common types of tours and how to use them. It finishes with a section noting that these methods may not be needed where there is an absence of association between any of the multiple variables.