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.

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.