Sometime you may be looking for k-means stopping criteria, based off of “Number of Reassigned Observations Within Cluster”.
H2O K-means implementation has following 2 stopping criteria in k-means:
- Outer loop for estimate_k – stop when relative reduction of sum-of-within-centroid-sum-of-squares is small enough
- lloyds iteration – stops when relative fraction of reassigned points is small enough
In R here is what you can do:
h2o.kmeans(x = predictors, k = 100, estimate_k = T, standardize = F, training_frame = train, validation_frame=valid, seed = 1234)
In Python here is what you can do:
iris_kmeans = H2OKMeansEstimator(k = 100, estimate_k = True, standardize = False, seed = 1234) iris_kmeans.train(x = predictors, training_frame = train, validation_frame=valid)
_estimate_k= TRUE _max_iterations = 100 (or a larger number.)
That’s it, enjoy!!