Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
The most common type of unsupervised learning is cluster analysis or clustering. It is the task of grouping a set of objects in such a way that object in the same group (cluster) are more similar to each other than to those in other groups.
There is also non-clustering unsupervised learning. An example thereof is identifying individual voices and music from a mesh of sounds. This is called the "Cocktail Party Algorithm".