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These days, the closest cluster of housing is about seven miles away.
Assign each data point to its closest cluster center.
Then an updating strategy for unclassified edges is designed to assign them to the closest cluster.
Then, all clusters that are at most ε further away than the closest cluster are determined.
For the next iteration, the distances are recalculated and users are assigned to the closest cluster.
Repeat: a. Assign each data point to its closest cluster center.
Such an approach is based on the following hypothesis according to Chandola, Banerjee and Kumar [23]: normal data instances lie close to their closest cluster centroid, while outliers are far away from their closest cluster centroid.
To overcome this limitation, a second category of clustering relies on the following hypothesis [23]: normal data instances lie close to their closest cluster centroid, while outliers are far away from their closest cluster centroid.
Here, b(x) measures the separation with the average distance of objects to alternative cluster, i.e., second closest cluster.
For each data sample, the closest cluster centre is determined and the sample is assigned to its upper approximation.
If no such cluster exists, the sample is assigned also to the lower approximation of the closest cluster.
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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