cluster analysis

Also found in: Dictionary, Thesaurus, Financial, Encyclopedia, Wikipedia.
Related to cluster analysis: factor analysis, Discriminant analysis

clus·ter a·nal·y·sis

a set of statistical methods used to group variables or observations into strongly interrelated subgroups.
Farlex Partner Medical Dictionary © Farlex 2012

clus·ter anal·y·sis

(klŭstĕr ă-nali-sis)
Set of statistical methods used to group variablesor observations into strongly interrelated subgroups.
Medical Dictionary for the Health Professions and Nursing © Farlex 2012

cluster analysis

a statistical method of arranging a set of observations into sub-sets, each of which groups together those observations having similarities.
Collins Dictionary of Biology, 3rd ed. © W. G. Hale, V. A. Saunders, J. P. Margham 2005
Mentioned in ?
References in periodicals archive ?
To address the primary aim of the study, a two-step cluster analysis was performed using the following variables: age, looked up health information, sex, health status, has trouble seeing, needs help with activities of daily living, and activities limited by difficulty remembering.
The results of the cluster analysis that was run on the weight parameters are more interesting.
Cluster Analysis Findings with the K-Average Technique
Cluster analysis was performed using the euclidean distances to study similarities among the genotypes using statistics software Statistica Version 6.0.
In cluster analysis by Ward method or dendrogram cottage in 717 distance, genotypes were put in 3 clusters (fig.
Whole-genome cluster analysis identified 9 additional isolates as part of this outbreak cluster (Table; Figure 1).
For the cluster analysis, data for Croatia was also included in the research, as it is the newest member state of the European Union starting with July 1, 2013.
Cluster analysis and PCA were conducted with the use of IBM SPSS 19.0 software.
In this section, we use two synthetic datasets to evaluate our proposed significance analysis method and cluster analysis method, respectively.
Integrating the longitudinal dimension reduction, cluster analysis, and neural network has also some relevant research applications.
The corn varieties grown in Dandong city were compared on the basis of the composition and some properties by using principal components analysis and Q-type cluster analysis. This could be useful for selecting the appropriate variety for final use suitability and provide a new pathway for evaluating the quality of maize varieties.
Spatial cluster analysis is not only an important part of spatial data mining, but also widely used in spatial data mining and in-depth study of one of the elements.