Does market basket analysis (association rule mining) primarily rely on conditional probabilities such as confidence P(B|A), along with support and lift, to find product affinities?

Introduction / Context:
Market basket analysis uncovers products that are frequently purchased together. It is used in retail, e-commerce, and content recommendation to drive cross-sell, promotions, and store layouts. The core technique is association rule mining, which evaluates co-occurrence patterns using measures grounded in probabilities and frequencies.

Given Data / Assumptions:

• Transactions are sets of items bought together.
• Rules have the form A -> B (if A then B).
• Key metrics: support, confidence, and lift.

Concept / Approach:
Support measures how frequently items occur together relative to all transactions. Confidence is a conditional probability P(B|A) — the probability that B occurs given A occurred. Lift compares the observed co-occurrence to what would be expected if A and B were independent (lift > 1 indicates positive association). Algorithms like Apriori or FP-Growth efficiently discover frequent itemsets and derive rules that meet thresholds for these metrics.

Step-by-Step Solution:
Aggregate transactions into itemsets.Compute support for candidate itemsets.Generate association rules A -> B and compute confidence = support(A,B)/support(A).Calculate lift = confidence/support(B) to gauge strength beyond chance.Filter rules by minimum thresholds and business relevance.

Verification / Alternative check:
Test discovered rules on holdout data; conditional probability patterns with strong lift should generalize, improving recommendations or basket-based promotions.

Why Other Options Are Wrong:
Averages/medians (option b) summarize quantities, not co-occurrence. Correlation (option c) does not capture directional rules. Regression (option d) models numeric targets rather than unordered co-purchase sets. Clustering (option e) groups customers or items but is not rule mining per se.

Common Pitfalls:
Focusing only on confidence and ignoring lift; missing seasonality; accepting spurious associations due to popularity skews; failing to test rules operationally.

Final Answer:
Applies — it uses conditional probability (confidence) with support and lift