Sentence examples for determine a cluster from inspiring English sources

As differences between two modalities of the same phobic material might not be of large effect size, a Monte Carlo simulation was used to determine a cluster size-based significance threshold [ 27].

We hypothesized that the induction of obesity in hypertensive rats would determine a cluster of dysfunctions enough to characterize the metabolic syndrome, as it is observed in humans, pointing out reduced expression of GLUT4 in insulin-sensitive tissues as a marker of insulin resistance.

Therefore, we used a simulation procedure (see Methods) to determine a cluster-size threshold (≥97 contiguous voxels, each at puncorr = 0.005); such clusters were unlikely (p<0.05) to occur by chance in our whole-brain analyses.

More importantly, we demonstrated an empirical procedure to determine a clustering mismatch threshold that minimizes the splitting of alleles into artifactual 1-haplotype clusters.

In particular, after the step 1 transformation, we determine each cluster sequentially instead of determining all clusters together, which means we search for small cells that have not been selected yet when determining a cluster.

where (mathcal {Y}^{n-1}) denotes the set of small cells included in from C 1 to C n−1. Equation (16) means that we determine each cluster sequentially, i.e., we search for small cells that have not been selected yet when determining a cluster according to the following equation.

Because repeated runs of k-means often generated dissimilar clusterings, the runs were combined to determine a consensus cluster number and composition.

Under the control of evolution-communication mechanism, the tissue-like membrane system cannot only find the most appropriate number of clusters but else determine a good clustering partitioning for a data set.

The goal of this study was to overcome three main shortcomings in using a single algorithm to determine a particular clustering of a phenomenon.

Partitioning around medoids (PAM) [ 24] or using square error distance [ 25] is then performed on this dissimilarity matrix to determine a representative clustering.

To determine an optimal clustering algorithm and the number of clusters that are most appropriate for our benchmark dataset (Human_eb), we used the R-package clvalid (Brock et al., 2008).