Sentence examples for ability of clustering from inspiring English sources
This CVI embodies the classification ability of clustering algorithm.
And this can be achieved by producing relatively stable clusters and by the ability of clustering method to maintain the current cluster structure stable as much as possible.
Whether large datasets will significantly improve the ability of clustering methods to detect existing structure when genetic differentiation is small remains to be tested with recent programs that have been specifically developed for rapidly processing population genomic data (Popescu et al. 2014; Raj et al. 2014).
This dataset surveyed 20 different cell lines, but to simplify our analyses we limited ourselves to evaluating the ability of clustering metrics to predict the optimal number of gene expression clusters between four cell types: embryonic stem cells, neural progenitor cells, neural stem cells and a teratoma-differentiated cell line.
The PCA score plot exhibited that no outlier sample was detected within the NMR data (δ 0.5-4.34 and δ 6.24-9.0), and the ability of clustering was fair to distinguish the metabolic profile of rat in different groups.
I therefore investigate the ability of cluster development and the collaborative approach to enhance local business development and application of Greenlandic businesses in mining projects.
The RSA modulation ability of clusters 3 and 4 was consistent with their respective RS values.
Since the informativeness metric simply measures the optimal number of clusters given a particular clustering algorithm, we chose not to explore the effect of the clustering method and instead focused only on the ability of cluster metrics to identify the optimal number of clusters within a given clustering result.
In our second set of experiments, we are interested in testing the ability of our clustered alignment algorithm to identify sets of genes that should share a common alignment.
We next sought to test the predictive ability of our clustering method by calculating the distribution of patients with particular outcomes across the clusters.
This paper illustrates and compares the ability of several clustering algorithms to correctly associate a given aggregate daily electrical load curve with its corresponding day of the week.
