The aim of unsupervised learning is to extract information from databases automatically. This process occurs without prior knowledge of the contents to be analyzed. Unlike supervised learning, there is no information on membership classes of the examples or generally on the output corresponding to a certain input. The goal is to get a model that is able to discover interesting properties, groups with similar characteristics (clustering) for instance, as shown in the following diagram:
Search engines are an example of applications of these algorithms. Given one or more keywords, they are able to create a list of links ...
