Sentence examples for capability of clustering from inspiring English sources

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We also compared SPIC with seven existing state-of-the-arts metrics for their capability of clustering motifs from the same group and retrieving motifs from a database on three datasets.

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In this paper, the reactive power output and control capability of clusters of inverters are discussed.

We use the unique capability of Cluster measurements in determining gradients to examine the associated current density, Lorentz force, and current dissipation/dynamo term.

It has 254 input channels and is designed to provide on-board trigger information to the first level trigger system of CMS, with the capability of cluster-width discrimination and high-pT track identification.

When there is a small amount of data, the distribution achieved with the Map task is seriously uneven, so there is a great difference in the utilization ratio of nodes and the computing capability of cluster cannot be fully exploited, while a lot of management overhead still exists, resulting in low performance.

This paper addresses the capability of clusters with tens and scores of satellites to perform the collision avoidance manoeuvre in the event of an external, kinetic impact threat, via distributed autonomous control and to return to its original configuration after the threat has passed.

Phase I explores the capability of fuzzy clustering methods for known number of clusters.

The reduction of the feature space dimension may have lowered the capability of the clustering algorithm, making any clustering performance differences less apparent.

According to the different measuring capabilities of cluster members, the master slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed; then the basic mathematical models, including coordinated relative measurement model and cluster centralized dynamics, are established respectively.

That comparable results were obtained from both our recA and rpoB studies demonstrates the capability of our clustering and phylogenetic analysis methods to potentially identify deeply branching organisms from environmental metagenomic sequences.

The size and the number sets exceeded the capability of existing clustering software.

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