Sentence examples for multivariate data clustering from inspiring English sources

The simulation results indicate that GGSA can effectively be used for multivariate data clustering.

We developed a novel data-driven exposure analysis approach based on data clustering techniques (C-EVA), and investigated its ability to discriminate between different simulated time lines of exposure compared with a conventional EVA using both univariate and multivariate approaches.

Multivariate analysis of phytoplankton community data clustered the sampled lakes into three assemblages, with ordination along axis 1 being significantly correlated to time and temperature (p < 0.006).

Poisson regression analysis was used for panel data, clustered by mother-ID and executed (in univariate and multivariate-mode) using population-averaged (PA) model [ 26, 27].

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.

In the multivariate data analysis section such clustering methods are explained in detail.

The well-known methods for handling multivariate data are related to dimension reduction, clustering, classification, and regression.

The principal points, which can be based on either raw curves or derivatives, effectively reduce the functional data to multivariate data, and enable subsequent k-means clustering.

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

Ordination is a method helpful to data clustering in multivariate analysis.

Here we study DVMM in the context of clustering of multivariate data.