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
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}?
Prof. Panchal:
