1. SAS Homework 3 Review Association rules mining
MIS2502
Data Analytics

2. SAS Homework 3 Review Association Rules
Using Transactions Data Set
Reject Store and Quantity – don’t need them
Assign ID to Transaction (Nominal) – this is our ‘basket’
Target to Product (Nominal) - this is what we’re trying to determine but now its not a Y/N(binary)
Step 8 = Transaction !
Add an Associations node (Model)
In Properties Export Rule by ID = Yes
Answer some questions regarding the Association Rules
Evaluate Support, Confidence and Lift

3. Set Up
Retail – associations between items purchased from Health/Beauty and Stationary.
400K + transactions collected from POS
Products
bar soap
bows
candy bars
deodorant
greeting cards
magazines
markers
pain relievers
pencils
pens
perfume
photo processing
prescription medications
shampoo
toothbrushes
toothpaste
wrapping paper
We are using 2

4. Association Rules - Diagram
Right Click and Run . Then view results…..

5. Process Set rule thresholds Define Item Sets
Read through Item Sets, create list of all possible association rules (X => Y) for the Item Sets
Compute Support, Confidence and Lift for each Rule
Support, frequency count of occurrence/ all transactions for both the individual items (X and, Y) and for the ItemSet (X,Y)
Confidence , strength of the association. How often Y appears in baskets that contain X
count (X=>Y)/count(X)
Expected Confidence X=>Y is the probability that one of the baskets has Y
Lift = s (X->Y)/s(X)*s(Y)
Or, in SAS, (confidence/expected confidence )
Drop those that don’t meet thresholds

6. Evaluating the Statistics
Support – frequency: % occurrence of ItemSet in data
Confidence – strength: % right hand occurs in left
Lift – dependence: prob of dependent occurrence /prob of random occurrence (>1)
Support v Confidence
Blue – 2 variable , - Red 3 variable
Confidence Plot Left v Right (red = high)
range at bottom
Confidence v Expected Confidence
Diff is Lift
<=Ordered by lift on x axis

7. Evaluating the Rules Table view>rules>rule table

8. In Class

9. In Class
1) Which rule(s) have the highest confidence? MUSICSTREAM ==> WEBSITE 2) Which rule(s) have the highest support? WEBSITE ==> PODCAST and PODCAST ==> WEBSITE 3) Which rule(s) have the highest lift? ARCHIVE ==> WEBSITE and WEBSITE ==> ARCHIVE 4) What are the two rule “pairs” in the list above? ARCHIVE ==> WEBSITE/WEBSITE ==> ARCHIVE and WEBSITE ==> PODCAST/PODCAST ==> WEBSITE 5) What other service “goes the most” with visiting the website for general information (WEBSITE)? In other words, what other service are WEBSITE visitors most likely to seek out? What statistic did you use to figure this out? ARCHIVE – LIFT is greater than 1. This implies that this isn’t just random chance – people are actively seeking out the WEBSITE if they’ve used the ARCHIVE.

10. In Class
6) What other service seems to “go the least” with visiting the website for general information (WEBSITE)? In other words, what other service are WEBSITE visitors least likely to seek out? What statistic did you use to figure this out?
PODCAST – LIFT is less than 1. This also implies that this isn’t just random chance – but this time, people who visit the web site are particularly unlikely to also download a podcast.
7) The rule MUSICSTREAM ==> WEBSITE has poor lift (i.e., less than 1), but the rule has the highest confidence. Explain how this is possible.
It could be that many people use both MUSICSTREAM and WEBSITE so it appears in visitors’ set of services a lot. However, there can still be a negative effect of one on the other. For example, I use the website a lot, and I use music streaming a lot, but I’m still less likely to do one if I’ve done the other – possibly they are substitutes.