Unsupervised learning , on the other hand , assumes training data that has not been hand-labeled , and attempts to find inherent patterns in the data that can then be used to determine the correct output value for new data instances .. A combination of the two that has recently been explored is semi-supervised learning , which uses a combination of labeled and unlabeled data ( typically a small set of labeled data combined with a large amount of unlabeled data ) .