sklearn.cluster
.k_means¶

sklearn.cluster.
k_means
(X, n_clusters, sample_weight=None, init='kmeans++', precompute_distances='auto', n_init=10, max_iter=300, verbose=False, tol=0.0001, random_state=None, copy_x=True, n_jobs=None, algorithm='auto', return_n_iter=False)¶ Kmeans clustering algorithm.
Read more in the User Guide.
Parameters: X : arraylike or sparse matrix, shape (n_samples, n_features)
The observations 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 Ccontiguous.
n_clusters : int
The number of clusters to form as well as the number of centroids to generate.
sample_weight : arraylike, shape (n_samples,), optional
The weights for each observation in X. If None, all observations are assigned equal weight (default: None)
init : {‘kmeans++’, ‘random’, or ndarray, or a callable}, optional
Method for initialization, default 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.
If a callable is passed, it should take arguments X, k and and a random state and return an initialization.
precompute_distances : {‘auto’, True, False}
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
n_init : int, optional, default: 10
Number of time the kmeans 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_iter : int, optional, default 300
Maximum number of iterations of the kmeans algorithm to run.
verbose : boolean, optional
Verbosity mode.
tol : float, optional
The relative increment in the results before declaring convergence.
random_state : int, RandomState instance or None (default)
Determines random number generation for centroid initialization. Use an int to make the randomness deterministic. See Glossary.
copy_x : boolean, optional
When precomputing distances it is more numerically accurate to center the data first. If copy_x is True (default), then the original data is not modified, ensuring X is Ccontiguous. 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, in this case it will also not ensure that data is Ccontiguous which may cause a significant slowdown.
n_jobs : int or None, optional (default=None)
The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details.algorithm : “auto”, “full” or “elkan”, default=”auto”
Kmeans algorithm to use. The classical EMstyle algorithm is “full”. The “elkan” variation is more efficient by using the triangle inequality, but currently doesn’t support sparse data. “auto” chooses “elkan” for dense data and “full” for sparse data.
return_n_iter : bool, optional
Whether or not to return the number of iterations.
Returns: centroid : float ndarray with shape (k, n_features)
Centroids found at the last iteration of kmeans.
label : integer ndarray with shape (n_samples,)
label[i] is the code or index of the centroid the i’th observation is closest to.
inertia : float
The final value of the inertia criterion (sum of squared distances to the closest centroid for all observations in the training set).
best_n_iter : int
Number of iterations corresponding to the best results. Returned only if return_n_iter is set to True.