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1.Obtain one of the data sets available at the UCI Machine Learning Repository and apply as many of the different visualization techniques described in the chapter as possible. The bibliographic notes and book Web site provide pointers to visualization software.
2.Identify at least two advantages and two disadvantages of using color to represent information visually.
3.What are the arrangement issues that arise concerning three-dimensional plots?
4.Discuss the advantages and disadvantages of using sampling to reduce the number of data objects that need to be displayed. Would simple random sampling (without replacement) be an excellent approach to sampling? Why or why not?
5.Describe how we would create visualizations to display information that describes the following types of systems.
a)Computer networks. Be sure to include both the network’s static aspects, such as connectivity, and the dynamic aspects, such as traﬃc.
b)The distribution of speciﬁc plant and animal species around the world fora speciﬁc moment in time.
c)Computer resources, such as processor time, main memory, and disk, for a set of benchmark database programs.
d)The change in the occupation of workers in a particular country over the last thirty years. Assume that we have yearly information about each person that also includes gender and level of education.
Be sure to address the following issues:
Representation. How will we map objects, attributes, and relationships to visual elements?
Arrangement. Are there any special considerations that need to be taken into account concerning 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 we handle a large number of attributes and data objects?
6.Describe one advantage and one disadvantage of a stem and leaf plot concerning a standard histogram.
7.How might we address the problem that a histogram depends on the number and location of the bins?
8.Describe how a box plot can give information about whether the value of an attribute is symmetrically distributed. What can we say about the symmetry of the distributions of the attributes shown in Figure 3.11?
9.Compare sepal length, sepal width, petal length, and petal width, using Figure3.12.
10.Comment on using a box plot to explore a data set with four attributes: age, weight, height, and income.
11.Give a possible explanation of why most of the petal length and width values fall in the buckets along the diagonal.
12.Use Figures 3.14 and 3.15 to identify a characteristic shared by the petal width and petal length attributes.
13.Simple line plots, such as that displayed in Figure 2.12 on page 56, which shows two-time series, can be used to eﬀectively display high-dimensional data. For example, in Figure 2.12, it is easy to tell that the two-time series frequencies are diﬀerent. What characteristic of time series allows the eﬀective visualization of high-dimensional data?
14.Describe the types of situations that produce sparse or dense data cubes. Illustrate with examples other than those used in the book.
15.How might we extend the notion of multidimensional data analysis so that the target variable is qualitative? In other words, what sorts of summary statistics or data visualizations would be of interest?
16.Construct a data cube from Table 3.14. Is this a dense or sparse data cube? If it is sparse, identify the empty cells.
17.Discuss the diﬀerences between dimensionality reduction based on aggregation and dimensionality reduction based on techniques such as PCA and SVD.
Need minimum 1000 words (Each question minimum 50 words except 5th question, For Question 5 minimum 200 words).
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