sklearn.cluster.MiniBatchKMeans  
class sklearn.cluster.MiniBatchKMeans(n_clusters=8, *, init='k-means++', max_iter=100, batch_size=100, verbose=0, compute_labels=True, random_state=None, tol=0.0, max_no_improvement=10, init_size=None, n_init=3, reassignment_ratio=0.01) [source]
 
Mini-Batch K-Means clustering. Read more in the User Guide.  Parameters 
 
n_clustersint, default=8 

The number of clusters to form as well as the number of centroids to generate.  
init{‘k-means++’, ‘random’}, callable or array-like of shape (n_clusters, n_features), default=’k-means++’ 

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.  
max_iterint, default=100 

Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics.  
batch_sizeint, default=100 

Size of the mini batches.  
verboseint, default=0 

Verbosity mode.  
compute_labelsbool, default=True 

Compute label assignment and inertia for the complete dataset once the minibatch optimization has converged in fit.  
random_stateint, RandomState instance or None, default=None 

Determines random number generation for centroid initialization and random reassignment. Use an int to make the randomness deterministic. See Glossary.  
tolfloat, default=0.0 

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).  
max_no_improvementint, default=10 

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.  
init_sizeint, default=None 

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, init_size= 3 * batch_size.  
n_initint, default=3 

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_ratiofloat, default=0.01 

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.    Attributes 
 
cluster_centers_ndarray of shape (n_clusters, n_features) 

Coordinates of cluster centers.  
labels_int 

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

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.  
n_iter_int 

Number of batches processed.  
counts_ndarray of shape (n_clusters,) 

Weigth sum of each cluster.  Deprecated since version 0.24: This attribute is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26).   
init_size_int 

The effective number of samples used for the initialization.  Deprecated since version 0.24: This attribute is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26).       See also  
KMeans


The classic implementation of the clustering method based on the Lloyd’s algorithm. It consumes the whole set of input data at each iteration.    Notes See https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf Examples >>> from sklearn.cluster import MiniBatchKMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
...               [4, 2], [4, 0], [4, 4],
...               [4, 5], [0, 1], [2, 2],
...               [3, 2], [5, 5], [1, -1]])
>>> # manually fit on batches
>>> kmeans = MiniBatchKMeans(n_clusters=2,
...                          random_state=0,
...                          batch_size=6)
>>> kmeans = kmeans.partial_fit(X[0:6,:])
>>> kmeans = kmeans.partial_fit(X[6:12,:])
>>> kmeans.cluster_centers_
array([[2. , 1. ],
       [3.5, 4.5]])
>>> kmeans.predict([[0, 0], [4, 4]])
array([0, 1], dtype=int32)
>>> # fit on the whole data
>>> kmeans = MiniBatchKMeans(n_clusters=2,
...                          random_state=0,
...                          batch_size=6,
...                          max_iter=10).fit(X)
>>> kmeans.cluster_centers_
array([[3.95918367, 2.40816327],
       [1.12195122, 1.3902439 ]])
>>> kmeans.predict([[0, 0], [4, 4]])
array([1, 0], dtype=int32)
 Methods  
fit(X[, y, sample_weight]) Compute the centroids on X by chunking it into mini-batches.  
fit_predict(X[, y, sample_weight]) Compute cluster centers and predict cluster index for each sample.  
fit_transform(X[, y, sample_weight]) Compute clustering and transform X to cluster-distance space.  
get_params([deep]) Get parameters for this estimator.  
partial_fit(X[, y, sample_weight]) Update k means estimate on a single mini-batch X.  
predict(X[, sample_weight]) Predict the closest cluster each sample in X belongs to.  
score(X[, y, sample_weight]) Opposite of the value of X on the K-means objective.  
set_params(**params) Set the parameters of this estimator.  
transform(X) Transform X to a cluster-distance space.    
fit(X, y=None, sample_weight=None) [source]
 
Compute the centroids on X by chunking it into mini-batches.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous.  
yIgnored 

Not used, present here for API consistency by convention.  
sample_weightarray-like of shape (n_samples,), default=None 

The weights for each observation in X. If None, all observations are assigned equal weight (default: None).  New in version 0.20.     Returns 
 self
   
  
fit_predict(X, y=None, sample_weight=None) [source]
 
Compute cluster centers and predict cluster index for each sample. Convenience method; equivalent to calling fit(X) followed by predict(X).  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

New data to transform.  
yIgnored 

Not used, present here for API consistency by convention.  
sample_weightarray-like of shape (n_samples,), default=None 

The weights for each observation in X. If None, all observations are assigned equal weight.    Returns 
 
labelsndarray of shape (n_samples,) 

Index of the cluster each sample belongs to.     
  
fit_transform(X, y=None, sample_weight=None) [source]
 
Compute clustering and transform X to cluster-distance space. Equivalent to fit(X).transform(X), but more efficiently implemented.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

New data to transform.  
yIgnored 

Not used, present here for API consistency by convention.  
sample_weightarray-like of shape (n_samples,), default=None 

The weights for each observation in X. If None, all observations are assigned equal weight.    Returns 
 
X_newndarray of shape (n_samples, n_clusters) 

X transformed in the new space.     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

Parameter names mapped to their values.     
  
partial_fit(X, y=None, sample_weight=None) [source]
 
Update k means estimate on a single mini-batch X.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Coordinates of the data points to cluster. It must be noted that X will be copied if it is not C-contiguous.  
yIgnored 

Not used, present here for API consistency by convention.  
sample_weightarray-like of shape (n_samples,), default=None 

The weights for each observation in X. If None, all observations are assigned equal weight (default: None).    Returns 
 self
   
  
predict(X, sample_weight=None) [source]
 
Predict the closest cluster each sample in X belongs to. In the vector quantization literature, cluster_centers_ is called the code book and each value returned by predict is the index of the closest code in the code book.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

New data to predict.  
sample_weightarray-like of shape (n_samples,), default=None 

The weights for each observation in X. If None, all observations are assigned equal weight (default: None).    Returns 
 
labelsndarray of shape (n_samples,) 

Index of the cluster each sample belongs to.     
  
score(X, y=None, sample_weight=None) [source]
 
Opposite of the value of X on the K-means objective.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

New data.  
yIgnored 

Not used, present here for API consistency by convention.  
sample_weightarray-like of shape (n_samples,), default=None 

The weights for each observation in X. If None, all observations are assigned equal weight.    Returns 
 
scorefloat 

Opposite of the value of X on the K-means objective.     
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
transform(X) [source]
 
Transform X to a cluster-distance space. In the new space, each dimension is the distance to the cluster centers. Note that even if X is sparse, the array returned by transform will typically be dense.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

New data to transform.    Returns 
 
X_newndarray of shape (n_samples, n_clusters) 

X transformed in the new space.     
 
 Examples using sklearn.cluster.MiniBatchKMeans
 
  Biclustering documents with the Spectral Co-clustering algorithm  

  Online learning of a dictionary of parts of faces  

  Compare BIRCH and MiniBatchKMeans  

  Empirical evaluation of the impact of k-means initialization  

  Comparison of the K-Means and MiniBatchKMeans clustering algorithms  

  Comparing different clustering algorithms on toy datasets  

  Faces dataset decompositions  

  Clustering text documents using k-means