Objectives: To identify novel asthma phenotypes using an unsupervised hierarchical cluster analysis.

Using hierarchical cluster analysis, we divided EBV-positive gastric carcinomas into two clusters.

These genes were further classified into 26 profiles by hierarchical cluster analysis.

Explanatory typologies were developed and then validated through hierarchical cluster analysis.

Conclusions: Five distinct clinical phenotypes of asthma have been identified using unsupervised hierarchical cluster analysis.

The results were analyzed using hierarchical cluster analysis and chi(2) test for trend.

Statistical analysis was performed making use of a factor analysis and a hierarchical cluster analysis.

Multidimensional scaling and hierarchical cluster analysis were used to produce the conceptual model (maps).

Unsupervised hierarchical cluster analysis showed distinct profiles between the bulky ESCC specimens and normal epithelial specimens.

We then examined the patterns of genetic correlation through hierarchical cluster analysis and by principal components analysis.

Also, unsupervised hierarchical cluster analysis revealed distinct profiles between the biopsy ESCC samples and normal epithelial specimens.

Both hierarchical cluster analysis and principal component analysis have been used to distinguish HP seed oil from different varieties.

Results: Unsupervised hierarchical cluster analysis revealed that the samples from LTx patients can be clearly distinguished from the comparison group.

The principal components analysis (PCA) and hierarchical cluster analysis showed a mixing of avocado trees from different districts.

Using simulated data, we found that factor analysis clearly identifies the number and structure of factors and outperforms hierarchical cluster analysis.

Symptom scores were compared between treatment groups, and hierarchical cluster analysis was used to depict clustering of symptoms at treatment end.