Mini-batch K-means Clustering

Variant of the KMeans algorithm which uses mini-batches to reduce the computation time

Documentation

Attributes

cluster_centers_

Coordinates of cluster centers.

inertia_

The value of the inertia criterion associated with the chosen partition if compute_labels is set to True. If compute_labels is set to False, it’s an approximation of the inertia based on an exponentially weighted average of the batch inertiae. The inertia is defined as the sum of square distances of samples to their cluster center, weighted by the sample weights if provided.

labels_

Labels of each point (if compute_labels is set to True).

Definition

Output ports

model model

Model

Configuration

Mini-batch size (batch_size)

Size of the mini batches. For faster computations, you can set the batch_size greater than 256 * number of cores to enable parallelism on all cores.

Changed in version 1.0: batch_size default changed from 100 to 1024.

Compute label assignment (compute_labels)

Compute label assignment and inertia for the complete dataset once the minibatch optimization has converged in fit.

Initialization method (init)

Method for initialization:

‘k-means++’ : selects initial cluster centroids using sampling based on an empirical probability distribution of the points’ contribution to the overall inertia. This technique speeds up convergence. The algorithm implemented is “greedy k-means++”. It differs from the vanilla k-means++ by making several trials at each sampling step and choosing the best centroid among them.

‘random’: choose n_clusters observations (rows) at random from data for the initial centroids.

If an array is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.

If a callable is passed, it should take arguments X, n_clusters and a random state and return an initialization.

Number of random samples (init_size)

Number of samples to randomly sample for speeding up the initialization (sometimes at the expense of accuracy): the only algorithm is initialized by running a batch KMeans on a random subset of the data. This needs to be larger than n_clusters.

If None, the heuristic is init_size = 3 * batch_size if 3 * batch_size < n_clusters, else init_size = 3 * n_clusters.

Maximum number of iterations (max_iter)

Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics.

Consecutive batches without improvement (max_no_improvement)

Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed inertia.

To disable convergence detection based on inertia, set max_no_improvement to None.

Number of clusters/centroids (n_clusters)

The number of clusters to form as well as the number of centroids to generate.

Number of random initializations (n_init)

Number of random initializations that are tried. In contrast to KMeans, the algorithm is only run once, using the best of the n_init initializations as measured by inertia. Several runs are recommended for sparse high-dimensional problems (see kmeans_sparse_high_dim).

When n_init=’auto’, the number of runs depends on the value of init: 3 if using init=’random’ or init is a callable; 1 if using init=’k-means++’ or init is an array-like.

New in version 1.2: Added ‘auto’ option for n_init.

Changed in version 1.4: Default value for n_init changed to ‘auto’ in version.

Random seed (random_state)

Determines random number generation for centroid initialization and random reassignment. Use an int to make the randomness deterministic. See random_state.

Reassignment ratio (reassignment_ratio)

Control the fraction of the maximum number of counts for a center to be reassigned. A higher value means that low count centers are more easily reassigned, which means that the model will take longer to converge, but should converge in a better clustering. However, too high a value may cause convergence issues, especially with a small batch size.

Tolerance (tol)

Control early stopping based on the relative center changes as measured by a smoothed, variance-normalized of the mean center squared position changes. This early stopping heuristics is closer to the one used for the batch variant of the algorithms but induces a slight computational and memory overhead over the inertia heuristic.

To disable convergence detection based on normalized center change, set tol to 0.0 (default).

Implementation

class node_clustering.MiniBatchKMeansClustering