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In unsupervised learning, the algorithm goes through the data itself and tries to come up with meaningful results. The result might be, for example, a set of clusters of data points that could be related within each cluster. That works better when the clusters don’t overlap. Machine learning algorithms explained | Martin Heller - InfoWorld

Unlike supervised learning, unsupervised learning is used with data sets without historical data. An unsupervised learning algorithm explores surpassed data to find the structure. This kind of learning works best for transactional data; for instance, it helps in identifying customer segments and clusters with certain attributes—this is often used in content personalization. Popular techniques where unsupervised learning is used also include self-organizing maps, nearest neighbor mappig, singular value decomposition, and K-Means clustering. Basically, online recommendations, identification of data outliers, and segment text topics are all examples of unsupervised learning. Machine Learning: What it is and Why it Matters | Priyadharshini @ simplilearn