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This script takes as inputs a cluster identifier, an instance, i.e., a map with values for all fields used by the cluster, and a positive count n
. It then:
Finds the centroid in the cluster closer to the given instance p
Selects within that centroid's dataset the n
instances that are closest to p
If there are less than n
rows in the centroid's dataset, missing instances are read from the next closest centroid.
This workflow uses flatline to compute the distance between p
and the centroid datasets (via the row-distance-squared
flatline function) and add an extra column to the dataset, and then creates a sample of the result, ordered by the computed distance.
The input instance can be specified using either field identifiers or field names.
Given a dataset and a categorical field, finds the minimum scale required to create class purity in the cluster with k = number of classes.