||This article may require cleanup to meet Wikipedia's quality standards. (May 2010)|
||This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. (February 2010)|
In machine learning, unsupervised learning refers to the problem of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning.
Unsupervised learning is closely related to the problem of density estimation in statistics. However unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data. Many methods employed in unsupervised learning are based on data mining methods used to preprocess data.
Approaches to unsupervised learning include:
- clustering (e.g., k-means, mixture models, hierarchical clustering),
- blind signal separation using feature extraction techniques for dimensionality reduction (e.g., Principal component analysis, Independent component analysis, Non-negative matrix factorization, Singular value decomposition). 
Among neural network models, the self-organizing map (SOM) and adaptive resonance theory (ART) are commonly used unsupervised learning algorithms. The SOM is a topographic organization in which nearby locations in the map represent inputs with similar properties. The ART model allows the number of clusters to vary with problem size and lets the user control the degree of similarity between members of the same clusters by means of a user-defined constant called the vigilance parameter. ART networks are also used for many pattern recognition tasks, such as automatic target recognition and seismic signal processing. The first version of ART was "ART1", developed by Carpenter and Grossberg (1988).
- Jordan, Michael I.; Bishop, Christopher M. (2004). "Neural Networks". In Allen B. Tucker. Computer Science Handbook, Second Edition (Section VII: Intelligent Systems). Boca Raton, FL: Chapman & Hall/CRC Press LLC. ISBN 1-58488-360-X.
- Acharyya, Ranjan (2008); A New Approach for Blind Source Separation of Convolutive Sources, ISBN 978-3-639-07797-1 (this book focuses on unsupervised learning with Blind Source Separation)
- Hinton, Geoffrey; Sejnowski, Terrence J. (editors) (1999); Unsupervised Learning: Foundations of Neural Computation, MIT Press, ISBN 0-262-58168-X (This book focuses on unsupervised learning in neural networks)
- Duda, Richard O.; Hart, Peter E.; and Stork, David G. (2001); Unsupervised Learning and Clustering, Chapter 10 in Pattern classification (2nd edition), p. 571, New York, NY: Wiley, ISBN 0-471-05669-3
See also 
- Artificial neural network
- Blind signal separation
- Cluster analysis
- Density estimation
- Data clustering
- Data mining
- Dimensionality reduction
- Expectation-maximization algorithm
- Generative topographic map
- Multilinear subspace learning
- Multivariate analysis
- Radial basis function network
- Self-organizing map
- Supervised learning