Cluster Analysis

cluster analysis, in statistics, set of tools and algorithms that is used to classify different objects into groups in such a way that the similarity between two objects is maximal if they belong to the same group and minimal otherwise. In biology, cluster analysis is an essential tool for taxonomy (the classification of living and extinct organisms). In clinical medicine, it can be used to identify patients who have diseases with a common cause, patients who should receive the same treatment, or patients who should have the same level of response to treatment. In epidemiology, cluster analysis has many uses, such as finding meaningful conglomerates of regions, communities, or neighbourhoods with similar epidemiological profiles when many variables are involved and natural groupings do not exist. In general, whenever one needs to classify large amounts of information into a small number of meaningful categories, cluster analysis may be useful.

Researchers are often confronted with the task of sorting observed data into meaningful structures. Cluster analysis is an inductive exploratory technique in the sense that it uncovers structures without explaining the reasons for their existence. It is a hypothesis-generating, rather than a hypothesis-testing, technique. Unlike discriminant analysis, where objects are assigned to preexisting groups on the basis of statistical rules of allocation, cluster analysis generates the groups or discovers a hidden structure of groups within the data.