русский
кластеризация португальский
agrupamento испанский
algoritmo de agrupamiento каталонский
clusterització de dades 
1
After age and gender stratification, a cluster analysis was also carried out.
2
Objectives: To identify novel asthma phenotypes using an unsupervised hierarchical cluster analysis.
3
A cluster analysis for 17 selected neoplasms was performed at micro-geographical level.
4
The results of cluster analysis were compatible with the indicated dietary patterns.
5
Unsupervised cluster analysis was performed, with expression data correlated to tumor characteristics.
6
The gene expression profiles were analyzed with special software and cluster analysis.
7
The FTIR images and the Raman maps were segmented by cluster analysis.
8
Using hierarchical cluster analysis, we divided EBV-positive gastric carcinomas into two clusters.
9
These genes were further classified into 26 profiles by hierarchical cluster analysis.
10
Objective: To validate the use of cluster analysis for characterizing population dietary patterns.
11
The primary goal in cluster analysis is to discover natural groupings of objects.
12
These genes were also subjected to cluster analysis according to their biological functions.
13
A cluster analysis grouped individuals with similar terpenoid blends into 56 compositional types.
14
Unsupervised cluster analysis revealed two distinct groups based on gene expression.
15
Hierarchical cluster analysis resulted in distinct separation of the two groups.
16
The dietary nutrient consumption pattern was identified by Two-step and K-means cluster analysis.