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MiniBatch KMeans clustering
This node has been automatically generated by wrapping the sklearn.cluster.k_means_.MiniBatchKMeans class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.
Parameters
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.
Control early stopping based on the relative center changes as measured by a smoothed, variancenormalized 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).
Method for initialization, defaults to 'kmeans++':
'kmeans++' : selects initial cluster centers for kmean 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.
Attributes
labels_ :
 Labels of each point (if compute_labels is set to True).
Notes
See http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf














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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

MiniBatch KMeans clustering This node has been automatically generated by wrapping the sklearn.cluster.k_means_.MiniBatchKMeans class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Attributes
labels_ :
Notes See http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf




Transform X to a clusterdistance space. This node has been automatically generated by wrapping the sklearn.cluster.k_means_.MiniBatchKMeans class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. In the new space, each dimension is the distance to the cluster
centers. Note that even if X is sparse, the array returned by
Parameters
Returns



Compute the centroids on X by chunking it into minibatches. This node has been automatically generated by wrapping the sklearn.cluster.k_means_.MiniBatchKMeans class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

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