ITS-632 Intro to Data Mining Dr. Sherri BrinsonDept. of Information Technology &School of Computer and Information SciencesUniversity of the Cumberlands Chapter 3 Assignment [Your Name Here]  1.

ITS-632 Intro to Data Mining

Dr. Sherri Brinson

Dept. of Information Technology &

School of Computer and Information Sciences

University of the Cumberlands 

Chapter 3 Assignment

[Your Name Here] 

1.      Obtain one of the data sets available at the UCI Machine Learning Repositoryand apply as many of the different visualization techniques described in thechapter as possible. The bibliographic notes and book Web site providepointers to visualization software.

2.      Identify at least two advantages and two disadvantages of using color to visuallyrepresent information.

1. What are the arrangement issues that arise with respect to three-dimensionalplots?

1. 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 a good approach to sampling? Why or why not?

1. Describe how you would create visualizations to display information that de-scribes the following types of systems.

a)      Computer networks. Be sure to include both the static aspects of thenetwork, 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)      The use of computer resources, such as processor time, main memory, anddisk, for a set of benchmark database programs.

d)      The change in occupation of workers in a particular country over the lastthirty years. Assume that you have yearly information about each personthat also includes gender and level of education.

Be sure to address the following issues:

·        Representation.How will you map objects, attributes, and relation-ships 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 dataobjects

6.      Describe one advantage and one disadvantage of a stem and leaf plot withrespect to a standard histogram.

7.      How might you address the problem that a histogram depends on the numberand location of the bins? 

8.      Describe how a box plot can give information about whether the value of anattribute is symmetrically distributed. What can you say about the symmetryof 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 the use of a box plot to explore a data set with four attributes:age, weight, height, and income. 

11.   Give a possible explanation as to why most of the values of petal length andwidth fall in the buckets along the diagonal in Figure 3.9. 

12.   Use Figures 3.14 and 3.15 to identify a characteristic shared by the petal widthand petal length attributes. 

13.   Simple line plots, such as that displayed in Figure 2.12 on page 56, whichshows 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 frequencies of the twotime series are different. What characteristic of time series allows the effectivevisualization 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 you extend the notion of multidimensional data analysis so that thetarget variable is a qualitative variable? In other words, what sorts of summarystatistics 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 cells that are empty.

17.   Discuss the differences between dimensionality reduction based on aggregationand dimensionality reduction based on techniques such as PCA and SVD.