Answer: (ii) Discover how strongly items are related beyond co-occurrence

Plain support/confidence-based association rules can be misleading: a rule can have high confidence purely because the consequent is very common overall, even if there's no real relationship (this is the classic "buys coffee ⇒ buys tea" trap when both items are just individually popular).

Correlation analysis (e.g., using lift or the chi-square test) checks whether the co-occurrence of items is statistically meaningful rather than coincidental:

$$\text{lift}(A, B) = \frac{P(A \cup B)}{P(A) \cdot P(B)}$$

• lift = 1 → A and B are independent (no real relationship, despite whatever confidence says).
• lift > 1 → positively correlated (occurring together more than expected by chance).
• lift < 1 → negatively correlated.

This is why correlation analysis "goes beyond" simple co-occurrence counting — it tells you whether the relationship is genuinely meaningful, not just frequent. It does not establish causation (option i) — correlation ≠ causation even in this context.