1. What are the different types of attributes? Provide examples of each attribute. (Minimum 150 words)2. Describe the components of a decision tree.Give an example problem and provide an example of each component in your decision making tree (minimum 150 words)3. Conduct research over the Internet and find an article on data mining. The article has to be less than 5 years old. Summarize the article in your own words. Make sure that you use APA formatting for this assignment. (350 – 500 words)Questions from attached files1. 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 thearrangement issues that arisewith respect tothree-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 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 tra?c.b) The distribution of speci?c plant and animal species around the world fora speci?c 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? Speci?c 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 objects6. 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 e?ectively display high-dimensional data. For example, in Figure 2.12 it is easy to tell that the frequencies of the two time series are di?erent. What characteristic of time series allows the e?ective 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 di?erences between dimensionality reduction based on aggregation and dimensionality reduction based on techniques such as PCA and SVD.