Unsupervised learning

In unsupervised learning, an input set is supplied to the system during the training phase. In contrast with supervised learning, the input objects are not labeled with their class. For classification, we assumed that we are given a training dataset of correctly labeled data. Unfortunately, we do not always have that advantage when we collect data in the real world.

For example, let's say you have a large collection of totally legal, not pirated, MP3 files in a crowded and massive folder on your hard drive. In such a case, how could we possibly group songs together if we do not have direct access to their metadata? One possible approach could be to mix various ML techniques, but clustering is often the best solution.

Now, what if you can build a clustering predictive model that helps automatically group together similar songs and organize them into your favorite categories, such as country, rap, rock, and so on? In short, unsupervised learning algorithms are commonly used in clustering problems. The following diagram gives us an idea of a clustering technique applied to solve this kind of problem:

Clustering techniques – an example of unsupervised learning

Although the data points are not labeled, we can still do the necessary feature engineering and grouping of a set of objects in such a way that objects in the same group (called a cluster) are brought together. This is not easy for a human. Rather, a standard approach is to define a similarity measure between two objects and then look for any cluster of objects that are more similar to each other than they are to the objects in the other clusters. Once we've done the clustering of the data points (that is, MP3 files) and the validation is completed, we know the pattern of the data (that is, what type of MP3 files fall in which group).