Answer: Supervised vs Unsupervised Classification

• Supervised classification: class labels are known in advance. A set of data objects with known class labels (the training set) is used to build a classification model; this model is then applied to assign class labels to new/unseen objects. In other words, the model is trained on pre-labelled data and the classes are predefined categories.

• Unsupervised classification: the class label is unknown. It attempts to discover the natural classes/clusters directly from the data itself (using clustering); the number of classes and their boundaries are not known in advance, and are derived by the algorithm (e.g., k-Means clustering, hierarchical clustering).

Known labels ──▶ Supervised classification ──▶ model ──▶ assign classes to new data
Unknown labels ──▶ Unsupervised classification ──▶ discover natural groupings/clusters from the data itself

In data mining: supervised classification = predicting a categorical class from a labelled training set; unsupervised classification = clustering (grouping similar objects without predefined labels).