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 visually represent information. 3. What are the arrangement issues that arise with respect to three-dimensional plots?

QUESTION

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 visually represent information.

3. What are the arrangement issues that arise with respect to 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 a good approach to sampling? Why or why not?

5. 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 the network, 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, 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 relation-ships to visual elements?
• Arrangement. Are there any special considerations that need to be taken 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

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

7. How might you 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 you 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 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 and width 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 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 frequencies of the two time series 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 you extend the notion of multidimensional data analysis so that the target variable is a qualitative variable? 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 cells that are empty.

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

ANSWER

Exploring Data Visualization Techniques: A Case Study with the UCI Machine Learning Repository

Introduction

Data visualization plays a crucial role in understanding and gaining insights from complex datasets. In this essay, we explore the application of various visualization techniques on a dataset obtained from the UCI Machine Learning Repository. By employing these techniques, we aim to uncover patterns, relationships, and distributions within the dataset, providing valuable insights for data analysis and decision-making.

Scatter plot

One of the fundamental visualization techniques is the scatter plot. By plotting the sepal length on the x-axis and sepal width on the y-axis, we can discern how different species of iris flowers are distributed based on these attributes (Srivastava, 2021). This plot enables us to identify any natural groupings or clusters, providing initial insights into the dataset’s structure.

Box plot

To understand the distribution of the dataset attributes, we employ box plots. By creating a box plot for each attribute (sepal length, sepal width, petal length, petal width), we can visualize important statistical summaries such as the median, quartiles, and outliers (Klodian, 2015). This technique offers a comprehensive overview of the data’s spread and central tendency, aiding in identifying potential anomalies or variations across different attributes.

Histogram

Histograms are invaluable for visualizing the distribution of values within a dataset. By creating individual histograms for each attribute, we can analyze the frequency and spread of values. This technique allows us to detect patterns, such as bimodal or skewed distributions, and assess the presence of clusters or gaps in the data.

Parallel coordinates plot

Parallel coordinates plots facilitate the visualization of multiple attributes simultaneously. By representing each data point as a line with axes corresponding to different attributes, we can observe patterns, trends, and relationships between variables (Heinrich & Weiskopf, 2015). This technique helps identify clusters, outliers, and the relative importance of different attributes in the dataset.

Heatmap

A heatmap provides a visual representation of the correlation between attributes. By assigning color gradients to different correlation strengths, we can quickly identify which attributes are positively or negatively correlated. This technique aids in understanding the interdependencies and relationships between variables, highlighting potential areas of interest for further analysis.

Conclusion

Data visualization techniques are vital for understanding and exploring complex datasets. By applying a range of visualization techniques to a dataset obtained from the UCI Machine Learning Repository, we can gain valuable insights into its structure, patterns, and relationships. Scatter plots, box plots, histograms, parallel coordinates plots, and heatmaps all offer unique perspectives and aid in data analysis and decision-making processes. Through the effective utilization of these techniques, researchers and analysts can derive meaningful insights, leading to informed conclusions and actions.

References

Heinrich, J., & Weiskopf, D. (2015). Parallel Coordinates for Multidimensional Data Visualization: Basic Concepts. Parallel Coordinates for Multidimensional Data Visualization: Basic Concepts, 17(3), 70–76. https://doi.org/10.1109/mcse.2015.55 

Klodian. (2015). Use box plots to assess the distribution and to identify the outliers in your dataset | R-bloggers. R-bloggers. https://www.r-bloggers.com/2015/08/use-box-plots-to-assess-the-distribution-and-to-identify-the-outliers-in-your-dataset/ 

Srivastava, R. (2021, December 11). Exploratory data analysis (EDA) on Iris Dataset using Python. Medium. https://medium.com/@rishav.jnit/exploratory-data-analysis-eda-on-iris-dataset-using-python-cadd850c1fc6