Fast K-Means Cluster Scoring uses a model output by Fast K-Means Clustering to score new data against centroids.
The input table for Fast K-Means Cluster Scoring has the same columns as Fast K-Means Clustering analyzed to build the model. The implicit assumption is that the underlying population distributions are the same.
In a single iteration, Fast K-Means Cluster Scoring assigns each input table row to a cluster, based on the Euclidean distance to each cluster centroid. It returns an output table of scores and optionally, a sample of rows in the output table.