We evaluated the correctness of contig clusters by computing two measures: the dominant genus - the percentage of the contigs that could be mapped to related genomes belonging to the same genus as the organism represented by the cluster; and the reference coverage - percentage of the total contig size of the pool that could be mapped to a genome from the dominant genus.
We then use methods derived from kernel and cokernel persistent homology to cluster the data points into different strata, and we prove a result which guarantees the correctness of our clustering, given certain topological conditions.
We then use methods derived from kernel and cokernel persistent homology to cluster the data points into different strata, and we prove a result which guarantees the correctness of our clustering, given certain topological conditions; some geometric intuition for these topological conditions is also provided.
The correctness of the clustering process can be quantified by the so-called sensitivity and specificity measures [ 33].
The idea is that while we cannot say something about the correctness of the resulting clusters for one name, we can definitely show that the clustering is wrong when a cluster is generated from publications from both names.
The third row presents the performance when we trained a disambiguation system with the sense inventory built by the clustering method (automatic) and measured the correctness of disambiguation results on the sense clusters built manually (gold-standard).
Just as in yeast, its categorical edge classification reduces the correctness of the ranking of its clusters, giving lower precision levels.
To confirm the correctness of this procedure, a hierarchical clustering of strains using either raw or normalized signal intensities of all probes was carried out.
Since it classifies edges categorically, many edges have similar scores that do not vary with classification accuracy; thus the ranking of clusters (based on their weighted-densities) does not correlate as well with their correctness, giving lower precision levels.
The results verify the correctness of adjacent speedup defined as the index of cluster efficiency.
Such cluster tries to evaluate the correctness of the system as perceived by the shoppers.
