A video rental store has 1,000 transactions with items A, B, C, and D. The probability that C and D are rented together in the same transaction is called what in market-basket analysis? Select the single best answer.

Introduction / Context:
Market-basket (association rule) analysis studies how items co-occur in transactions. Three core metrics are commonly used: support, confidence, and lift. Understanding these is crucial for interpreting association rules and prioritizing actionable insights in retail and recommendation systems.

Given Data / Assumptions:

• Transactions: 1,000 total.
• Items: A, B, C, D.
• Event of interest: C and D in the same basket.

Concept / Approach:

Support measures how frequently an itemset appears in the dataset: support(C,D) = count(C ∩ D) / total_transactions. Confidence measures conditional probability for a rule: confidence(C → D) = support(C,D) / support(C). Lift evaluates the strength of a rule relative to independence: lift(C → D) = confidence(C → D) / support(D).

Step-by-Step Solution:

Verification / Alternative check:

Compute an example: if 120 of 1,000 baskets contain {C,D}, then support({C,D}) = 120 / 1,000 = 0.12 (12%). Confidence and lift need additional marginals and are not required here.

Why Other Options Are Wrong:

Basic probability: vague wording; the established term is support. Confidence: conditional on antecedent, not joint itemset frequency. Lift: ratio that adjusts confidence by expected co-occurrence under independence.

Common Pitfalls:

Confusing support with confidence; using high-confidence rules with very low support, which may be unreliable; ignoring lift when ranking rules for actionability.

Final Answer:

support