Sentence examples for multivariate clustering techniques from inspiring English sources

The LC analysis outperforms multivariate clustering techniques, as it is based on a probability model that enables the use of inference on the outcomes.

Using multivariate clustering techniques we found 5 clusters of compounds as shown in Figure 4.

In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes.

Multivariate clustering techniques calculate a measure of similarity between gene expression profiles and are used for unsupervised analysis of gene expression data (i.e. hierarchical clustering, K-means clustering, self-organizing maps) [ 56- 59].

To examine this hypothesis, a comprehensive panel of biomarkers with relevance to BC, described above, has been applied to a large and well-characterised series of BC, using IHC and different multivariate clustering techniques, to identify the key molecular classes, phase 1 of NPI+ classification.

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.

One possibility to overcome this issue is to limit the number of groups to test, thanks to the use of multivariate analysis (see [ 8]) or clustering techniques for instance.

These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients.

Hands and Everitt [18] compared five hierarchical clustering techniques (single linkage, complete linkage, average, centroid, and Ward's method) on multivariate binary data.

These techniques include unsupervised learning and clustering techniques, supervised neural network-based classification, and deep data analysis of physically relevant multivariate statistics data.

In conclusion, we have previously applied different clustering techniques with validity indices, and used cluster consensus to derive a classification that is robust across different multivariate procedures.