Data Mining


Chapter 2 Exercises:

8. Discuss why a document-term matrix is an example of a data set that has asymmetric discrete or asymmetric continuous features.

9. Many sciences rely on observation instead of (or in addition to) designed experiments.Compare the data quality issues involved in observational science with those of experimental science and data mining.

10. Discuss the difference between the precision of a measurement and the terms single and double precision, as they are used in computer science, typically to represent floating-point numbers that require 32 and 64 bits, respectively.

18. This exercise compares and contrasts some similarity and distance measures.
(a) For binary data, the L1 distance corresponds to the Hamming distance;that is, the number of bits that are different between two binary vectors.The Jaccard similarity is a measure of the similarity between two binary vectors. Compute the Hamming distance and the Jaccard similarity between the following two binary vectors.x = 0101010001y = 0100011000
(b) Which approach, Jaccard or Hamming distance, is more similar to the Simple Matching Coefficient, and which approach is more similar to the cosine measure? Explain. (Note: The Hamming measure is a distance,while the other three measures are similarities, but don’t let this confuse you.)
(c) Suppose that you are comparing how similar two organisms of different species are in terms of the number of genes they share. Describe which measure, Hamming or Jaccard, you think would be more appropriate for comparing the genetic makeup of two organisms. Explain. (Assume that each animal is represented as a binary vector, where each attribute is 1 if a particular gene is present in the organism and 0 otherwise.)
(d) If you wanted to compare the genetic makeup of two organisms of the same species, e.g., two human beings, would you use the Hamming distance,the Jaccard coefficient, or a different measure of similarity or distance?Explain. (Note that two human beings share > 99.9% of the same genes.)

22. Discuss how you might map correlation values from the interval [-1,1] to the interval [0,1]. Note that the type of transformation that you use might depend on the application that you have in mind. Thus, consider two applications:clustering time series and predicting the behavior of one time series given another.

27. Show that the distance measure defined as the angle between two data vectors,x and y, satisfies the metric axioms given on page 70. Specifically, d(x, y) : arccos(cos(x,y)).

Chapter 3 Exercises:

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 traffic.
(b) The distribution of specific plant and animal species around the world for a 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 relationships 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 data objects?

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

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