Answer: (iv) Group data points into clusters with high intra-group similarity and low inter-group similarity

Clustering is an unsupervised task: its objective is to partition the data so that objects inside the same cluster are as similar as possible (high intra-cluster similarity) while objects belonging to different clusters are as dissimilar as possible (low inter-cluster similarity). Option (iv) states exactly this criterion, and it is formalised through similarity/distance measures and minimisation of the within-cluster sum of squares.

'Find hidden patterns in data' (ii) is far too vague — it describes data mining as a whole, not clustering specifically. Classifying data into known categories (i) is classification, and predicting future values (iii) is prediction; both are supervised tasks requiring labelled data, so they are wrong.