Answer: (i) Hidden relationships among items in large datasets

Association rule mining finds interesting co-occurrence relationships between items in large transactional datasets, expressed as rules of the form:

$$\text{Item}_A \Rightarrow \text{Item}_B \quad [\text{support } s, \text{confidence } c]$$

Example: {Bread, Butter} ⇒ {Milk} [support=20%, confidence=75%] — meaning 20% of all transactions contain all three items, and 75% of transactions with Bread+Butter also contain Milk.

It is not regression (ii — a prediction technique), decision-tree rules (iii — a classification technique), or hierarchical clusters (iv — a clustering technique); it's specifically about discovering frequent co-occurrence / hidden relationships among items.