Cluster Analysis

Clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure. Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics.
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A statistical technique that identifies clusters of stocks whose returns are highly correlated within each cluster and relatively uncorrelated between clusters. Cluster analysis has identified groupings such as growth, cyclical, stable and energy stocks.

a multivariate statistical technique used to identify entities with similar characteristics from those without them.

The term cluster analysis (first used by Tryon, 1939) actually encompasses a number of different classification algorithms which can be used to develop taxonomies (typically as part of exploratory data analysis ). For example, biologists have to organize the different species of animals before a meaningful description of the differences between animals is possible. According to the modern system employed in biology, man belongs to the primates, the mammals, the amniotes, the vertebrates, and the animals. Note how in this classification, the higher the level of aggregation the less similar are the members in the respective class. Man has more in common with all other primates (e.g., apes) than it does with the more "distant" members of the mammals (e.g., dogs), etc. For information on specific types of cluster analysis methods, see Joining (Tree Clustering) , Two-way Joining (Block Clustering) , and K-means Clustering .
See the Cluster Analysis chapter for more general information; see also the Classification Trees chapter.

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