1. Jerry Post Copyright © 2003 1 Database Management Systems: Data Mining Market Baskets Association Rules

2. DATA DDAATTAA Mining MiningDDAATTAA Mining Mining 2 Association/Market Basket  Examples  What items are customers likely to buy together?  What Web pages are closely related?  Others?  Classic (early) example:  Analysis of convenience store data showed customers often buy diapers and beer together.  Importance: Consider putting the two together to increase cross- selling.

3. DATA DDAATTAA Mining MiningDDAATTAA Mining Mining 3 Association Challenges  If an item is rarely purchased, any other item bought with it seems important. So combine items into categories.  Some relationships are obvious.  Burger and fries.  Some relationships are meaningless.  Hardware store found that toilet rings sell well only when a new store first opens. But what does it mean? ItemFreq. 1 “ nails2% 2” nails1% 3” nails1% 4” nails2% Lumber50% ItemFreq. Hardware15% Dim. Lumber20% Plywood15% Finish lumber15%

4. DATA DDAATTAA Mining MiningDDAATTAA Mining Mining 4 Association Measure: Confidence  Does A  B?  If a customer purchases A, will they purchase B?

5. DATA DDAATTAA Mining MiningDDAATTAA Mining Mining 5 Association Measure: Support  Does the existing data support the rule?  What percentage of baskets contain both A and B?

6. DATA DDAATTAA Mining MiningDDAATTAA Mining Mining 6 Association Measure: Lift  How does the association rule compare to the null hypothesis (the A item exists without the B item)?  What is the likelihood of finding the second item (B) in any random basket?

7. DATA DDAATTAA Mining MiningDDAATTAA Mining Mining 7 Association Details (two items)  Rule evaluation (A implies B)  Support for the rule is measured by the percentage of all transactions containing both items: P(A ∩ B)  Confidence of the rule is measured by the transactions with A that also contain B: P(B | A)  Lift is the potential gain attributed to the rule—the effect compared to other baskets without the effect. If it is greater than 1, the effect is positive: P(A ∩ B) / ( P(A) P(B) ) P(B|A)/P(B)  Example: Diapers implies Beer  Support: P(D ∩ B) =.6P(D) =.7P(B) =.5  Confidence: P(B|D) =.857= P(D ∩ B)/P(D)=.6/.7  Lift: P(B|D) / P(B) = 1.714=.857 /.5

8. DATA DDAATTAA Mining MiningDDAATTAA Mining Mining 8 Example (Marakas) 1. Frozen pizza, cola, milk 2. Milk, potato chips 3. Cola, frozen pizza 4. Milk, pretzels 5. Cola, pretzels Transaction data