Mini-batch K-means Clustering

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_iter

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

• 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.

• 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.

• 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.

• init

  Method for initialization, defaults to ‘k-means++’:

  ‘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 k observations (rows) at random from data for the initial centroids.

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

• compute_labels

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

• 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.

• 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).

• random_state

  If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

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). The inertia is defined as the sum of square distances of samples to their nearest neighbor.

Inputs: Outputs:

model : model

Model

Output ports:

model:

model

Model

Configuration:

n_clusters

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

max_iter

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

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.

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.

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.

init

Method for initialization, defaults to ‘k-means++’:

‘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 k observations (rows) at random from data for the initial centroids.

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

compute_labels

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

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.

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).

random_state

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

class node_clustering.MiniBatchKMeansClustering