Data Mining Final (25 Pts)
Name:
Section 1: Data Mining Overview (5 points)
Discuss whether or not each of the following activities is a data mining task.
Example Question: Dividing the customers of a company according to their
profitability.
Example Answer: No. This is an accounting calculation, followed by the
application of a threshold. However, predicting the profitability of a new
customer would be data mining.
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Computing the total sales of a company.
Sorting a student database based on student identification numbers.
Predicting the outcomes of tossing a (fair) pair of dice.
Predicting the future stock price of a company using historical records.
Monitoring the heart rate of a patient for abnormalities.
Monitoring seismic waves for earthquake activities.
Extracting the frequencies of a sound wave.
Section 2: Classification (5 points)
In no fewer then 200 words describe what is meant by “classification” in
Data Mining. What are some of the challenges and strengths of these
techniques? Briefly describe some classification examples as applied to data
mining.
Section 3: Clustering (5 points)
In no fewer then 200 words describe what is meant by “clustering” in Data
Mining. What are some of the challenges and strengths of these techniques?
Briefly describe some clustering examples as applied to data mining.
Section 4: Artificial Intelligence (5 points)
In no fewer then 200 words describe how Artificial Intelligence is used in
Data Mining. How are Data Mining techniques different from Artificial
Intelligence techniques? Give an example of how Artificial Intelligence can be
used for a Data Mining application. In your example explain why it is
considered Data Mining and not Artificial Intelligence.
Section 5: Statistical Analysis (5 points)
In no fewer then 200 words describe how Statistical Analysis is used in Data
Mining. What makes the technique Data Mining and not just statistical
analysis? Provide an example of how statistics can be used for a data mining
application.
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