Cluster analysis

Cluster analysis or clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense.

Cluster Analysis in practice
Theories behind Cluster Analysis techniques are usually simple, leading to the idea that unsupervised classification is an easy business. It is not so.
Nature of the variables ...

Cluster analysis is an unsupervised learning technique, and we cannot observe the (real) number of clusters in the data. However, it is reasonable to replace the usual notion (applicable to supervised learning) of "accuracy" with that of "distance.

Cluster Analysis
The process of grouping a set of physical or abstract objects into classes of similar objects is called clustering.
The following are typical requirements of clustering in data mining: ...

Cluster analysis is based on a mathematical formulation of a measure of similarity. There are a number of characteristics that distinguish different approaches to cluster analysis.
Cluster Analysis Characteristics: ...

Difference of Cluster Analysis and Discriminant Analysis
By Kardi Teknomo, PhD.
About ...

It encloses subdisciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition), ...

Bortolan, G., et al. 1991. "ECG classification with neural networks and cluster analysis." Proceedings Computers in Cardiology. Held: Venice, Italy, 23-26 Sept.

The use of feature analysis to identify an image of an object. May involve techniques such as statistical pattern recognition, Bayesian analysis, classification, cluster analysis, and analysis of texture and edges. See machine vision.

See also: Data mining, Distribution, Regression, Variance, Estimation