Cluster Analysis Introductory Overview - General Purpose

Cluster Analysis

The term cluster analysis (first used by Tryon, 1939) actually encompasses a number of different classification algorithms. A general question facing researchers in many areas of inquiry is how to organize observed data into meaningful structures, that is, to develop taxonomies. 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.

See also, Statistical Significance Testing and Area of Application.

For a review of the general categories of cluster analysis methods, see Joining (Tree Clustering), Two-way Joining (Block Clustering), and K-means Clustering.

See also Exploratory Data Analysis and Data Mining Techniques, Data Mining, and Classification Trees.