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Chapter 2 Exercises:
9. Many sciences rely on observation instead of (or in addition to) designed experiments. Compare the data quality issues involved in observational science with those of experimental science and data mining.
10. Discuss the difference between the precision of a measurement and the terms single and double precision, as they are used in computer science, typically to represent floating-point numbers that require 32 and 64 bits, respectively.
22. Discuss how you might map correlation values from the interval [-1,1] to the interval [0,1]. Note that the type of transformation that you use might depend on the application that you have in mind. Thus, consider two applications:clustering time series and predicting the behavior of one time series given another.
27. Show that the distance measure defined as the angle between two data vectors,x and y, satisfies the metric axioms given on page 70. Specifically, d(x, y) : arccos(cos(x,y)).
Chapter 3 Exercises:
5. Describe how you would create visualizations to display information that describes the following types of systems.
(a) Computer networks. Be sure to include both the static aspects of the network, such as connectivity, and the dynamic aspects, such as traffic.
(b) The distribution of specific plant and animal species around the world for a specific moment in time.
(c) The use of computer resources, such as processor time, main memory, and disk, for a set of benchmark database programs.
(d) The change in occupation of workers in a particular country over the last thirty years. Assume that you have yearly information about each person that also includes gender and level of education.
Be sure to address the following issues:
* Representation. How will you map objects, attributes, and relationships to visual elements?
* Arrangement. Are there any special considerations that need to betaken into account with respect to how visual elements are displayed? Specific examples might be the choice of viewpoint, the use of transparency,or the separation of certain groups of objects.
* Selection. How will you handle a large number of attributes and data objects?