Unsupervised learning is a machine learning technique that, starting from a series of inputs (system experience), is able to reclassify and organize on the basis of common characteristics to try to make predictions on subsequent inputs. Unlike supervised learning, only unlabeled examples are provided to the learner during the learning process, as the classes are not known a priori but must be learned automatically.
The following diagram shows three groups labeled from raw data:
From this diagram, it is possible to notice that the system has identified three groups on the basis of a similarity, which in this case is due to proximity. In general, unsupervised learning tries to identify the internal structure of data to reproduce it.
Typical examples of these algorithms are search engines. These programs, given one or more keywords, are able to create a list...
