Mini-batch K-means Clustering

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

Documentation

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

Configuration:

• n_clusters

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

• 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.

• batch_size

  Size of the mini batches. For faster compuations, 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.

• init

  Method for initialization:

  ‘k-means++’ : selects initial cluster centers for k-mean clustering in a smart way to speed up convergence. See section Notes in k_init for more details.

  ‘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.

• compute_labels

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

• max_iter

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

• 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).

• 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.

• 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.

• 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.

• random_state

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

Attributes:

• cluster_centers_

  Coordinates of cluster centers.

• labels_

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

• 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.

Input ports:

Output ports:

modelmodel

Model

Definition

Input ports

Output ports

model

model

Model

class node_clustering.MiniBatchKMeansClustering