Unsupervised Learning

Unsupervised learning is a method of machine learning whereby the algorithm is presented with examples from the input space only and a model is fit to these observations. For example, a clustering algorithm would be a form of unsupervised learning.
Sewell (2006)

"Unsupervised learning is a method of machine learning where a model is fit to observations. It is distinguished from supervised learning by the fact that there is no a priori output. In unsupervised learning, a data set of input objects is gathered. Unsupervised learning then typically treats input objects as a set of random variables. A joint density model is then built for the data set."
Wikipedia (2006)

"The problem of unsupervised learning involved learning patterns in the input when no specific output values are supplied."
Russell and Norvig (2003) page 650

In the unsupervised learning problem, we observe only the features and have no measurements of the outcome. Our task is rather to describe how the data are organized or clustered."
Hastie, Tibshirani and Friedman (2001), page 2

"In unsupervised learning or clustering there is no explicit teacher, and the system forms clusters or “natural groupings” of the input patterns. “Natural” is always defined explicitly or implicitly in the clustering system itself; and given a particular set of patterns or cost function, different clustering algorithms lead to different clusters. Often the user will set the hypothesized number of different clusters ahead of time, but how should this be done? How do we avoid inappropriate representations?"
Duda, Hart and Stork (2001), page 17