1. Market Basket Analysis 포항공대 산업공학과 PASTA Lab. 석사과정 20012557 신원영

2. What is Market Basket Analysis? Finding useful information in ‘ Market Basket ’ Useful information – Who customers are – Which products tend to be purchased together – Why some products tend to be purchased together Useful information like “ If Item A then Item B ” is called ‘ Association rule ’

3. Association Rules (1) The clear and useful result of market basket analysis Only one result in association rules are strongly recommended – “ If diapers and Thursday,then beer ” is more useful than “ If Thursday,then diapers and beer ” Three Rules – The trivial, the useful, and the inexplicable rules – The trivial rule is already known and simple like “ If paint, then paint brushes ”

4. Association Rules (2) - The Useful Rule Contains high quality,actionable information Once the patter(rule) is found, it is often not hard to justify – Example ; Rules like “ On Thursday, Customer who purchase diapers are likely to purchase beer ”  Young couples prepare for the weekend by stocking up on diapers for the infants and beer for dad Can be applied to Market layout – Same Ex.) Placing other baby products within sight of the beer

5. Association Rules (3) - The Inexplicable rules Inexplicable rules give a new fact but no information about consumer behavior and future actions. – Example) “ When a new hardware store opens, one of the most commonly sold items is toilet toilet rings ” Inexplicable rules can be flukes More investigation might give explanation

6. The Basic Process  Choosing the right set of items (by using taxonomies and virtual items)  Generating rules (by deciphering the counts in the co-occurrence matrix)  Overcoming the practical limits (imposed by thousands or tens or tens of thousands of items appearing in combinations)

7. Taxonomies - Choosing the right set of items (1) Why? – To reduce too many item combinations – When the items occur in about the same number of times in the data, the analysis produce the best results How to find the appropriate level? – Consider frequency (roll up rare items to higher levels to help to generalize items) – Consider importance (roll down expensive item to lower levels)

8. Virtual Items - Choosing the right set of items (2) Items that cross product boundaries – So not to appear in the product taxonomy – Possible to include information about transactions themselves(ex.seasonal information) There is danger of using Virtual Items – Redundant Rules; If Coke in summer, then Coke – If diet soda and coke then pizza  If diet coke then pizza

9. Generating rules (1)  Gathering transactions for selected items ( including virtual items)  Making Co-Occurrence Matrix  Finding the most frequent combination from the matrix  Making a distinction between ‘ condition ’ and ‘ result ’ from that combination – the two keywords ; confidence, improvment

10. Generating rules (2) Example of transactions CustomerItems 1Orange juice, soda 2Milk, Orange juice, window Cl 3Orange juice, detergent 4Orange juice, detergent,soda 5Window Cl, soda

11. Generating rules (3) Example of Co-Occurrence Matrix OJWindow Cl.MilkSodaDetergent OJ4 (0.8) 1 (0.2) 1 (0.2) 2 (0.4) 1 (0.2) Window Cl1 (0.2) 2 (0.4) 1 (0.2) 1 (0.2) 0 Milk1 (0.2) 1 (0.2) 1 (0.2) 00 Soda2 (0.4) 1 (0.2) 03 (0.6) 1 (0.2) Detergent1 (0.2) 001 (0.2) 2 (0.4)

12. Generating rules (4) Assume most common combination ‘ A,B,C ’ CombinationProbabilityCombinationProbability A45%A and B25% B42.5%A and C20% C40%B and C15% A and B and C5%

13. Generating rules (5) Which is Result between A,B,C? Setting a result on the basis of ‘ confidence ’ Confidence of rule “ If condition then result ” – P(Result|Condition) = P(Result and Condition)/P(Condition) Confidence of rule “ If AB then C ” = P(ABC|AB) Association Rule P(condition)P(condition and result) confidence If AB then C 25%5%0.20 If AC then B 20%5%0.25 If BC then A 15%5%0.33

14. Generating rules (6) What if P(R) > P(R|C) (= Confidence) ? ‘ Improvement ’ tells how much better a rule is than just prediction the result Improvement = P(R|C) / P(R) = P(RC)/P(R)P(C) – If Improvement > 1  the rule is better – If Improvement < 1  “ If C,then NOT R ” is better RuleconfidenceImprovement If BC then A 0.330.74 If BC then Not A 0.671.22 If A then B 0.591.31

15. Overcoming the practical limits Exponential growth as problem size increases – On menu with 100 items,how many combinations are there?  161,700 (3 items ) How to solve the problem of Big Data? – To use the taxonomy ; generalize items that can meet criterion – To use Pruning ; throw out item or combination of items that do not meet criterion. “ Minimum support pruning ” is the most common pruning mechanism

16. Strengths of Market Basket Analysis It produces clear and understandable results – because the results are association rules It supports both directed and undirected data mining It can handle transactions themselves The computations it uses are simple to understand – The calculation of confidence,and improvement is simple

17. Weaknesses of Market Basket Analysis It requires exponentially more computational effort as the problem size grows – number of items,complexity of the rules It has limited support for data attribute – Virtual items make rules more expressive It is difficult to determine the right number of items It discounts rare items

18. Application of Market Basket Analysis It can suggest store layouts It can tell which products are amenable to promotion It is used to compare stores It can be applied to time-series problems