When solving an unsupervised learning problem, we only observe the features and have no measurements of the outcome. Instead of the prediction of future outcomes or the inference of relationships among variables, the task is to find structure in the data without any outcome information to guide the search process. 

Often, unsupervised algorithms aim to learn a new representation of the input data that is useful for some other tasks. This includes coming up with labels that identify commonalities among observations, or a summarized description that captures relevant information while requiring data points or features. Unsupervised learning algorithms also differ from supervised learning algorithms in the assumptions they make about the nature of the structure they are aiming to discover.