Unsupervised Learning

In words...

In unsupervised learning, the computer is given a data set of unlabeled patterns from which it should extract "knowledge". This knowledge can take different forms, each giving rise to a different learning problem.

Clustering aims at classifying the data into a number of groups containing similar patterns.

Density estimation aims at learning a generative model of the data, which could be used for instance to generate new data instances or estimate the probability that a pattern falls within a given region.

Dimensionality reduction methods can also be seen as unsupervised learning ones.

In pictures...

Abstract view of unsupervised learning

Hover over the elements of the diagram to get additional information.

In maths...

The goal of unsupervised learning is to extract knowledge from a data set of $N$ unlabeled input vectors, $$ \{ \g x_1, \dots, \g x_N \} \in \X^N . $$ This knowledge can take different forms, such as groups of similar patterns or a probabilistic model of the data, typically a probability density function $$ p_X(x). $$