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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
Selects within that centroid's dataset the
ninstances that are closest to
If there are less than
nrows 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.
Find the global field importance across a cluster
Please see the readme for more information.
Given a dataset and a categorical field, finds the minimum scale required to create class purity in the cluster with k = number of classes.