Answer: (ii) Apriori

Apriori is the classic algorithm for mining frequent itemsets. It uses the Apriori property — any subset of a frequent itemset must itself be frequent — to generate candidate itemsets level by level and prune unpromising ones against a minimum support threshold. The frequent itemsets it returns are then used to generate association rules evaluated by support, confidence and lift.

K-means is a clustering algorithm, a decision tree is a supervised classification/prediction model, and Naive Bayes is a probabilistic classifier. None of them produces frequent itemsets, so (i), (iii) and (iv) are wrong.