Answer: (iii) Classification, Clustering

Predictive data mining builds a model that predicts a target (labelled) attribute for new or unseen cases, and classification does exactly that: it learns a model from labelled training data and assigns class labels to future records. Descriptive data mining discovers patterns with no target variable to predict; clustering groups objects purely by their inherent similarity in unlabelled data, so it is descriptive.

Association rule mining (e.g. Apriori over market-basket data) is also a descriptive technique, not a predictive one, so options (i) and (ii) are wrong; option (iv) simply reverses the two roles.