Introduction to Data Mining (ITS632)

Introduction to Data Mining

Description/Paper Instructions

Introduction to Data Mining (ITS632)

Department of Information Technology & Telecommunications

Computer and Information Sciences School

Cumberlands University is a public university in Cumbria, England.

Assignment for Chapter 3

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1. Use as many of the visualization techniques taught in the chapter to visualize one of the data sets accessible at the UCI Machine Learning Repository. Pointers to visualization tools can be found in the bibliographic notes and on the book’s Web site.

2. Determine at least two benefits and two drawbacks of utilizing color to visually display data.

3. What are the problems with three-dimensional plots in terms of arrangement?

4. Compare and contrast the benefits and drawbacks of employing sampling to limit the amount of data objects that must be displayed. Is simple random sampling (without replacement) a viable sampling strategy? Why do you think that is?

5. Describe how you would construct visuals to display data from the systems listed below.

Computer networks (a) Make sure to cover both the static and dynamic components of the network, such as connection and traffic.

b) The global distribution of specific plant and animal species at a given point in time.

c) The usage of computer resources for a group of benchmark database applications, such as CPU time, main memory, and disk.

d) The shift in employees’ occupations in a certain country over the last thirty years. Assume you have yearly information about each person, including gender and educational level.

Make certain to address the following concerns:

Representation is a word that has a lot of different meanings. How will you map visual components to objects, properties, and relationships?

Organization. Is there anything specific that needs to be considered when it comes to how visual elements are displayed? The usage of transparency, for example, or the isolation of certain groupings of objects are all examples of specific viewpoints.

The process of choosing. What strategy will you use to deal with a huge number of attributes and data objects?

6. Compare and contrast the advantages and disadvantages of a stem and leaf plot to a regular histogram.

7. How would you approach the issue of a histogram being dependent on the number and position of bins?

8. Explain how a box plot can be used to determine whether an attribute’s value is symmetrically distributed. What do you think about the symmetry of the attribute distributions in Figure 3.11?

9. Figure 3.12 can be used to compare sepal length, sepal width, petal length, and petal width.

10. Comment on how a box plot was used to investigate a data set with four variables: age, weight, height, and income.

11. Give a feasible explanation for why the majority of petal length and width values in Figure 3.9 fall into the buckets along the diagonal.

12. Use Figures 3.14 and 3.15 to find a feature that the petal width and petal length attributes have in common.

13. Simple line plots, such as the two-time series plot shown in Figure 2.12 on page 56, can be utilized to effectively illustrate high-dimensional data. It’s easy to see that the frequencies of the two time series are different in Figure 2.12. What feature of time series makes it possible to visualize high-dimensional data effectively?

14. Describe the scenarios that result in sparse or dense data cubes. Use instances that aren’t from the book to illustrate your point.

15. How would you broaden the concept of multidimensional data analysis to include qualitative variables as the goal variable? What kinds of summary statistics or data visualizations would be of interest, in other words?

16. Create a data cube using Table 3.14. Is this a data cube that is dense or sparse? Identify the empty cells if the data is sparse.

17. Distinguish between aggregation-based dimensionality reduction and dimensionality reduction using techniques like PCA and SVD.

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