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The clustered data is fed to the neural network that is used to predict the different types of faults present in the transformers.
Pivotal to analysing clustered data is the understanding that clustering can be informative or incidental.
An approach to analyze clustered data is the use of a multilevel or random effects regression analysis.
Since hierarchically clustered data is not necessarily associated with a similarity measure, the selection is based on a graph theoretic notion of cohesive clusters.
However, it is insufficiently well recognised that one method of adjusting the bootstrap to deal with clustered data is only valid in large samples.
An important issue in dealing with clustered data is that independent variables may have different between-person and within-person associations with the outcome.
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The properties of the best set of clustered data are then utilized to develop the respective FCs.
In diagnostic trials, clustered data are obtained when several subunits (e.g., organs or vessels) of the same patient are observed where no, several, or all subunits may be diseased or non-diseased as classified by a gold standard.
The clustered data are passed into InCHlib in a JSON compliant input data format.
Clustered data are present here because multiple animals from the same strain are assessed.
Clustered data are displayed at a heat map using Java TreeView [31].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com