Sentence examples for precision of clusters from inspiring English sources

Furthermore, the precision of clusters increases with the number of algorithms that generated them: among clusters generated by a single algorithm, the highest precision is 20%; clusters generated by two algorithms have a precision of 28%; the precision increases to 78% among the clusters generated by all six algorithms.

$$\end{documentt} C matches P = true if size (C ) > 3 ∧ size (P ) > 3 ∧ Jaccard (P, C ) ≥ lg _ match or size (C ) ≤ 3 ∧ size (P ) ≤ 3 ∧ Jaccard (P, C ) ≥ sm _ match false otherwise The precision of clusters is calculated only among those clusters that do not match a training complex, to eliminate the bias of the supervised approaches for predicting training complexes well.

The most notable improvement is in the precision of cluster locations detected, as DP algorithm tends to output fewer false-positive outbreak cells than the other algorithms.

(C, D, E ) Precision of Cluster 1 solutions (diagonal values) and average RMSD between Cluster 1 solutions (off-diagonal values) computed for the four ensembles, considering the whole Mediator complex, the Middle module, and the Tail module.

In this respect, a particularly attractive feature of ENIGMA is that it finds very few modules in the non-modular data (3 ± 1 modules containing on average 5 genes each, precision of clustering result = 0.27), in contrast to ISA and SAMBA, which recover 78±± 5 modules (containing on average 27 genes) and 127 ± 2 modules (containing on average 16 genes), respectively.

The precision of the clusters increases as they are generated by more clustering algorithms: from a maximum of 16% when generated by a single algorithm, to 37% when generated by all six algorithms.

An indication of the purity (precision) of the clusters in the augmented training data set can be viewed in Table 4.

This estimator is quite naive when the cluster size varies, because small clusters are given the same weight as large clusters and information about the precision of the cluster-specific estimates is ignored.

The use of naming conventions increase the effectiveness of the precision of our clustering algorithm because more variables could be clustered and identified with a particular concern.

The precision of each cluster was calculated as the average RMSD between the individual solutions and the cluster-center solution, defined as the solution with the minimal sum of the RMSD's to the other solutions in the cluster.

To compute precision of a cluster, we find the ratio of the reads that are wrongly assigned to the cluster to the total number of reads in the cluster.