Supervised machine learning is used to group data based only on outputs and includes clustering, representation learning, and density estimation.

Supervised machine learning relies on labeled datasets, which means it learns patterns from input data that are associated with specific outputs or labels. This methodology involves training algorithms to predict outcomes based on input features, such as regression and classification tasks.

The statement in question incorrectly categorizes clustering, representation learning, and density estimation as forms of supervised machine learning. In fact, these techniques are primarily aspects of unsupervised learning, where the algorithm identifies patterns and structures in data without prior labels or outputs. Clustering, for example, groups similar data points together based on inherent similarities, while density estimation aims to model the distribution of the data without referencing specific output labels.

Therefore, the definition provided in the question does not align with the characteristics of supervised machine learning, making the statement false.