The Mini-Batch k-means is a variant of the k-means algorithm which uses mini-batches to reduce the computation time, while still attempting to optimize the same objective function.
Number of times the algorithm will be run with different initial centroids. The greatest performing centroid will be used for the returned model.
Multiple runs are highly recommended if using sparse data to prevent poor performance.
Used to generate reproducible results. 0 or no value means that no known seed is used (results will not be fully reproducible)
Allow DSS to use sparse matrices to train the model
This may help reduce RAM and CPU usage
Sparse matrices is enabled but only {{mlTaskDesign.modeling.mini_batch_kmeans_clustering.n_init}} centroid(s) will be tested. It is strongly recommended to test multiple centroids (at least 5) when working with sparse matrices.