Description
1 Recitation Exercises
These exercises are to be found in: Introduction to Data Mining, 2nd
Edition by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar.
1.1 Chapter 5
Exercises: 2,6,8,9,12,13,20
2 Practicum Problems
These problems will primarily reference the lecture materials and the examples
given in class using Python. It is suggested that a Jupyter/IPython notebook
be used for the programmatic components.
2.1 Problem 1
Load the Online Retail dataset (Online Retail.xlsx) from the UCI Machine
Learning Repository into Python using a Pandas dataframe. Using the apriori
module from the MLxtend library, generate Frequent Itemsets for all transactions for the country of France. What itemset has the largest support? Set
the minimum support threshold to 5% and extract frequent itemsets, and use
them as input to the association rules module. Use each of the confidence and
lift metrics to extract the association rules with the highest values, respectively.
What are the antecedents and consequents of each rule? Is the rule with the
highest confidence the same as the rule with the highest lift? Why or why not?
2.2 Problem 2
Load the Extended Bakery dataset (75000-out2-binary.csv) into Python using a Pandas dataframe. Calculate the binary correlation coefficient Φ for the
Chocolate Coffee and Chocolate Cake items. Are these two items symmetric
binary variables? Provide supporting calculations. Would the association rules
{Chocolate Coffee} =⇒ {Chocolate Cake} have the same value for Φ as
{Chocolate Cake} =⇒ {Chocolate Coffee}?
CS 422
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