The phrase "cluster assessment" is correct and usable in written English.
It can be used in contexts related to evaluating or analyzing a group of items, data points, or subjects that are grouped together.
Example: "The cluster assessment revealed significant patterns in the data that were previously unnoticed."
Alternatives: "group evaluation" or "batch analysis."
Cluster assessment was performed on the basis of a structured evaluation sheet (see Electronic Supplementary Material 2).
It should be emphasized that, a vast collection of cluster assessment methodologies developed for microarray studies such as Gene Ontology-based cluster validation [ 53] can, in principle, be applied to SAGE data analysis.
The sheet also included a number of categories for potential alternative explanations of the non-SSFFC clusters, such as 'switch of brand' and 'medication error', in order to support a structured approach to the cluster assessment and to detect patterns that might help to improve the performance of the algorithm.
At issue is whether the public, legislators and others should expect improvements in identifying causes of cancer by conducting cluster assessments and/or allocating additional resources to these types of investigations, and if not, whether other benefits of conducting cancer cluster investigations are commensurate with the resources expended.
The clusters assessment, along with the number of molecules found in a given cluster for each scorpion, which assigns for the venom and structural family complexities, respectively, was used to generate a phenetic correlation tree for positioning these species.
These clustering assessment measures are defined as follows: Purity = f 11 f 11 + f 10 (13) Efficiency = f 11 f 11 + f 01 (14) Jaccard = f 11 f 11 + f 10 + f 01 (15).
In Table 1 all the available algorithms for classification, numeric prediction and clustering assessments are summarized.
A recent study has demonstrated that Kulldorff's scan statistic correctly assesses the statistical significance of the most likely cluster, but assessment of secondary clusters is more conservative [ 37].
Different comparison methods such as clustering dispersion indicator, Davies-Bouldin indicator, stability index are utilized for cluster validity assessment [23, 27].
Three plots were randomly selected from each cluster for assessment.
It is widely acknowledged that cluster quality assessment is inherently multi-dimensional.
