Answer: (i) Assigning data to predefined classes

Classification is a supervised learning task in which a model is first trained on a labelled training set (tuples for which the class label is already known), and then the learned model is used to assign new, unseen data objects to one of the predefined classes.

For example, an e-mail classifier is trained on a set of e-mails already labelled "spam" or "not-spam", and thereafter predicts the label of every incoming e-mail.

Why the others are wrong:

• (ii) Dividing data into clusters — this is clustering, an unsupervised task with no predefined class labels.
• (iii) Summarizing data — this describes data characterization/summarization, not classification.
• (iv) Cleaning data — this is data preprocessing, not classification.

So classification is fundamentally about assigning data objects to predefined classes.