Calculate the Support, Confidence and Lift for each the following rules (1) { Bread }>{ Milk } (2) { Beer }{ Eggs } (3) { Beer, Bread }> Milk } (4) { Bread, Eggs } DDiapers } (5) { Milk, Bread } Eggs, Coke } (6) { Beer, Milk\} Bread, Diapers } (7) { Bread, Diapers }> Beer, Milk } (8) { Bread, Eggs - Milk, Diapers } (9) Bread, Milk, Eggs\} -> \{Diapers\} (10) { Bread, Eggs, Coke\} -> \{Diapers, Beer\} Using interpretations of support and confidence, which items do you suggest should be put together on the shelves? (5 Points) Execute the following command before anything. rm(list=1s()) You will use the dataset "IncomeESL" from the library "arules" for this question (1) Load the libraries "arules" and "arulesViz" (You may need to install the packages). Load the dataset "IncomeESL". Remove the incomplete cases in the dataset (Hint: Use either na.omit() or complete.cases()). What is this dataset about? How many rows does this dataset have? (Hint: Use the help command on this one) (1 Point) (2) Run the Apriori algorithm on the dataset with minimum support =0.2 and confidence =0.9. Store the output in a variable "rules_1". How many rules do you have? Inspect the top 10 rules sorted by decreasing order of confidence. ( 1 Point) (3) What if you change the minimum support to 0.3 and confidence to 0.95. How many rules do you have now? Store the output in "rules_2". Inspect the top 10 rules sorted by decreasing order of lift. (1 Point) (4) Use the scatter plot on "rules_1" with support on x-axis and confidence on y-axis shaded by lift. What do you infer from the plot? (1 Point) (5) Use the parallel co-ordinates plot for "rules_2". Which rule seems to have the highest confidence here? (1 Point)