Unsupervised learning
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's 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's able to discover interesting properties; groups with similar characteristics (clustering), for instance. Search engines are an example of an application of these algorithms. Given one or more keywords, they're able to create a list of links related to our search.
The validity of these algorithms depends on the usefulness of the information they can extract from the databases. ...
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