Sentence examples for multivariate datasets from inspiring English sources

Keim et al. [[13]] reviewed and summarized recent visualization techniques to deal with large multivariate datasets.

Its application is often intended to help interpretation of multivariate datasets.

Non-directed analyses typically consist of ad hoc scalar extraction, an approach which substantially simplifies the original, highly multivariate datasets (many time points, many vector components).

Different from the Z-score, box plots make no assumptions about the data distribution model, however, for multivariate datasets, it is possible to perform a pairwise distance measure.

In the case of large multivariate datasets, some of these natural clusters can be big enough such that the furthest data vectors are statistically far away from the mean.

When considering the future of ecological immunology analyses, the potential importance of methods that can unite several multivariate datasets also comes to light.

This paper developed a new MEDE technique for multivariate dataset.

Principal Component Analysis (PCA) is a method that projects a multivariate dataset to a new coordinate system by determining the eigenvectors and eigenvalues of a matrix, facilitating visualization of the data.

To create the artificial population, a large multivariate dataset containing NFI field observations and ALS metrics was generated using a copula function fitted to the empirical observations, and then it was generalized over the study area using satellite imagery and nearest neighbor imputations.

Principal Component Analysis (PCA) was used for visualizing trends in the multivariate dataset, and identifying outliers.

Global shark biogeographic units were identified by using a hierarchical cluster analysis, a standard method used to find structure in a multivariate dataset.