cluster analysis

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clus·ter a·nal·y·sis

a set of statistical methods used to group variables or observations into strongly interrelated subgroups.

cluster analysis

Etymology: AS, clyster, growing together; Gk, analyein, to loosen
(in statistics) a complex technique of data analysis of numeric scale scores that produces clusters of variables related to one another. The technique is performed with computer software or statistics programs.

clus·ter anal·y·sis

(klŭstĕr ă-nali-sis)
Set of statistical methods used to group variablesor observations into strongly interrelated subgroups.

cluster analysis

a statistical method of arranging a set of observations into sub-sets, each of which groups together those observations having similarities.

cluster analysis,

n a complex statistical technique of data analysis of numeric scale scores, producing clusters of variables related to one another.

cluster

1. in epidemiological terms a naturally occurring group of similar units, e.g. animals which resemble each other, with respect to one or more variables, more than animals in different groups do, or a group of cases of a single disease in time or space.
2. assembly of claw and teat cups, as part of a milking machine.

cluster analysis
1. statistical methods used to group variables or observations into strongly interrelated subgroups.
2. a statistical analysis of the relationships between clusters in time and/or space.
cluster fly
see polleniarudis.
cluster sampling
see cluster sampling.
References in periodicals archive ?
To avoid such an outcome, we removed the ubiquitous species that had formed clusters when using all species combined and then ran all cluster analyses again with the remaining species to identify additional assemblages.
Cluster analyses were explored with the occurrence probability of fakies on each tow (x) set equal to the frequency of occurrence of each rebuilding species (Table 1).
This step was undertaken by conducting 3 sets of cluster analyses that had been stratified 1) by using tree regression, 2) by using 1[degrees] latitudinal strata, and 3) by departure port.
To confirm that the classification criteria reflected cluster analyses, the consistency between the final peer grouping and each previous clustering was assessed with weighted kappa statistics.
This study demonstrated the procedure for performing cluster analyses of the characteristics of general hospitals that provide health care under the Korea NHI program so that the systematic risks within a peer group are similar and peer-group-classification criteria can be developed.
The cluster analyses revealed contrasting results regarding the number of profiles generated in comparison with previous findings within school students.
To see if the clusters from the above-mentioned cluster analyses varied based on individual difference variables, the following analysis was conducted.
The same procedure (26 agglomerative hierarchical clustering procedures using the centroid method and Squared Euclidean distance) was followed with the cluster analyses per couple for the restaurant task (See Table 3).
From the results of cluster analyses, initial MC was found to be the best sorting parameter.