Sentence examples for use of multivariate data from inspiring English sources

It is worth noting that combined use of multivariate data (i.e., seismic, geodetic, tsunami, and so on) possibly compensates for the weakness of single variable data analysis during great events with complex disasters.

A number of examples illustrate the use of multivariate data analysis in chemistry: The origin of an organic material found in traces on a Neolithic statuette is identified from GC/MS data.

Geometric quantiles are an extension of univariate quantiles in a multivariate set-up that uses the geometry of multivariate data clouds.

Hierarchical clustering [ 57, 58] is the widely used algorithm for clustering of multivariate data.

Variable selection represents an important category of dimensionality reduction methods frequently used in the analysis of multivariate data within data mining and multivariate statistics.

We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome.

PCA is a popular technique used to find the major component of multivariate data, in DNA microarray analyses, it is used to find the gene groups that cooperatively change expressions over several experiments (Brunet et al., 2004), where PCA is done in gene space.

Finite mixtures are often used to perform model based clustering of multivariate data sets.

The normality of multivariate data was tested using probability plot of PCA model.

Clustering of multivariate data was visualized using a 2-dimensional biplot projection of patients and outcomes [ 32].

Principal components analysis is one of a number of multivariate data analysis techniques commonly used in analyzing sensory panel data.