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Understanding Market Basket Analysis: Insights and Challenges in Association Rules

Market Basket Analysis (MBA) uncovers purchasing patterns by analyzing transaction data to determine what items customers are likely to buy together. Through association rules, businesses can identify relationships, such as the classic case of customers buying diapers and beer together, and leverage this information for effective cross-selling strategies. However, challenges like rare purchases and meaningless correlations must be addressed. Key measures such as support, confidence, and lift help quantify these associations, providing valuable insights for decision-making in retail.

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Understanding Market Basket Analysis: Insights and Challenges in Association Rules

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  1. Database Management Systems:Data Mining Market Baskets Association Rules

  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. 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?

  4. Association Measure: Confidence • Does A  B? • If a customer purchases A, will they purchase B?

  5. Association Measure: Support • Does the existing data support the rule? • What percentage of baskets contain both A and B?

  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. 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) = .6 P(D) = .7 P(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. Example (Marakas) Transaction data 1. Frozen pizza, cola, milk 2. Milk, potato chips 3. Cola, frozen pizza 4. Milk, pretzels 5. Cola, pretzels

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