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We provide a clustering framework suitable to perform clustering and evaluate clustering performance on a large dataset.
Shown in Table 1 is the microphone specific clustering performance.
Thus, selecting appropriate features affects clustering performance positively.
Clustering performance is mostly dependent on the text features' characteristics.
Evaluating clustering performance is still a challenge to date for a number of reasons.
Fig. 6 Clustering performance in the function of the similarity threshold.
b Average number of common channels per cluster Fig. 7 Clustering performance (transmission range 200 m).
After that, we can tune the values of p for the better clustering performance [21].
Fig. 6 Clustering performance (transmission range 100 m). a Average number of clusters.
We compared the clustering performance and the explanatory power of four algorithms in conformer dataset.
This indicates that the choice of (tau) has very little impact on the clustering performance.
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agglomeration performance
gathering performance
cluster performance
clustering results
describing performance
the aggregation performance
representing performance
clustering running
clustering yields
clustering yielding
the grouping performance
clustering outputs
clusters performance
clustering performs
clustering performed
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com