This is from SNHU DAT-510 Lab-6, please help to answer below questions in bold.

3. Inspect the rule using the following statement:

> inspect(basket_rules) 

4. Review the output.

5. State the generated rule and the support, confidence and the lift thresholds for the rule

Mine the Rules for the Groceries Data:

> #mine rules > rules <- apriori(Groceries, parameter=list(support=0.001, confidence=0.5)) 

Note the values used for the parameter list.

How many rules are generated?

Extract the Rules in which the Confidence Value is >0.8 and high lift:

1. Execute the following commands:

> subrules <- rules[quality(rules)$confidence > 0.8] > plot(subrules, control = list(jitter=2)) > inspect(subrules) 

2. Review the results.

3. How many sub-rules did you extract?

These rules are more valuable for the business.

4. Extract the top three rules with high threshold for the parameter "lift" and plot.

> #Extract the top three rules with high lift > rules_high_lift <- head(sort(rules, by="lift"), 3) > inspect(rules_high_lift) > plot(rules_high_lift,method="graph", + control=list(type="items")) 

5. List the rules and the value of the parameters associated with these rules: