Unsupervised classification: the goal is to discover groups of similar features within the data, where the algorithm identifies clusters based on the 1st law of geography: places closer together are more likely to be similar to one another than features farther apart. This can be done using k-means clustering, in which the cluster identities can only be determined after classification is complete.