sklearn.cluster.KMeans  
class sklearn.cluster.KMeans(n_clusters=8, *, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='deprecated', verbose=0, random_state=None, copy_x=True, n_jobs='deprecated', algorithm='auto') [source]
 
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.  
n_initint, default=10 

Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.  
max_iterint, default=300 

Maximum number of iterations of the k-means algorithm for a single run.  
tolfloat, default=1e-4 

Relative tolerance with regards to Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence.  
precompute_distances{‘auto’, True, False}, default=’auto’ 

Precompute distances (faster but takes more memory). ‘auto’ : do not precompute distances if n_samples * n_clusters > 12 million. This corresponds to about 100MB overhead per job using double precision. True : always precompute distances. False : never precompute distances.  Deprecated since version 0.23: ‘precompute_distances’ was deprecated in version 0.22 and will be removed in 1.0 (renaming of 0.25). It has no effect.   
verboseint, default=0 

Verbosity mode.  
random_stateint, RandomState instance or None, default=None 

Determines random number generation for centroid initialization. Use an int to make the randomness deterministic. See Glossary.  
copy_xbool, default=True 

When pre-computing distances it is more numerically accurate to center the data first. If copy_x is True (default), then the original data is not modified. If False, the original data is modified, and put back before the function returns, but small numerical differences may be introduced by subtracting and then adding the data mean. Note that if the original data is not C-contiguous, a copy will be made even if copy_x is False. If the original data is sparse, but not in CSR format, a copy will be made even if copy_x is False.  
n_jobsint, default=None 

The number of OpenMP threads to use for the computation. Parallelism is sample-wise on the main cython loop which assigns each sample to its closest center. None or -1 means using all processors.  Deprecated since version 0.23: n_jobs was deprecated in version 0.23 and will be removed in 1.0 (renaming of 0.25).   
algorithm{“auto”, “full”, “elkan”}, default=”auto” 

K-means algorithm to use. The classical EM-style algorithm is “full”. The “elkan” variation is more efficient on data with well-defined clusters, by using the triangle inequality. However it’s more memory intensive due to the allocation of an extra array of shape (n_samples, n_clusters). For now “auto” (kept for backward compatibiliy) chooses “elkan” but it might change in the future for a better heuristic.  Changed in version 0.18: Added Elkan algorithm     Attributes 
 
cluster_centers_ndarray of shape (n_clusters, n_features) 

Coordinates of cluster centers. If the algorithm stops before fully converging (see tol and max_iter), these will not be consistent with labels_.  
labels_ndarray of shape (n_samples,) 

Labels of each point  
inertia_float 

Sum of squared distances of samples to their closest cluster center.  
n_iter_int 

Number of iterations run.      See also  
MiniBatchKMeans


Alternative online implementation that does incremental updates of the centers positions using mini-batches. For large scale learning (say n_samples > 10k) MiniBatchKMeans is probably much faster than the default batch implementation.    Notes The k-means problem is solved using either Lloyd’s or Elkan’s algorithm. The average complexity is given by O(k n T), were n is the number of samples and T is the number of iteration. The worst case complexity is given by O(n^(k+2/p)) with n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii, ‘How slow is the k-means method?’ SoCG2006) In practice, the k-means algorithm is very fast (one of the fastest clustering algorithms available), but it falls in local minima. That’s why it can be useful to restart it several times. If the algorithm stops before fully converging (because of tol or max_iter), labels_ and cluster_centers_ will not be consistent, i.e. the cluster_centers_ will not be the means of the points in each cluster. Also, the estimator will reassign labels_ after the last iteration to make labels_ consistent with predict on the training set. Examples >>> from sklearn.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
...               [10, 2], [10, 4], [10, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
>>> kmeans.labels_
array([1, 1, 1, 0, 0, 0], dtype=int32)
>>> kmeans.predict([[0, 0], [12, 3]])
array([1, 0], dtype=int32)
>>> kmeans.cluster_centers_
array([[10.,  2.],
       [ 1.,  2.]])
 Methods  
fit(X[, y, sample_weight]) Compute k-means clustering.  
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.  
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 k-means clustering.  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. If a sparse matrix is passed, a copy will be made if it’s not in CSR format.  
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.  New in version 0.20.     Returns 
 self

Fitted estimator.     
  
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.     
  
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.    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.KMeans
 
  Release Highlights for scikit-learn 0.23  

  Demonstration of k-means assumptions  

  Vector Quantization Example  

  K-means Clustering  

  Color Quantization using K-Means  

  Empirical evaluation of the impact of k-means initialization  

  Comparison of the K-Means and MiniBatchKMeans clustering algorithms  

  A demo of K-Means clustering on the handwritten digits data  

  Selecting the number of clusters with silhouette analysis on KMeans clustering  

  Clustering text documents using k-means