sklearn.cluster
.k_means¶

sklearn.cluster.
k_means
(X, n_clusters, init='kmeans++', precompute_distances='auto', n_init=10, max_iter=300, verbose=False, tol=0.0001, random_state=None, copy_x=True, n_jobs=1, algorithm='auto', return_n_iter=False)[source]¶ Kmeans clustering algorithm.
Read more in the User Guide.
Parameters: X : arraylike or sparse matrix, shape (n_samples, n_features)
The observations to cluster.
n_clusters : int
The number of clusters to form as well as the number of centroids to generate.
max_iter : int, optional, default 300
Maximum number of iterations of the kmeans algorithm to run.
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.
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’: generate k centroids from a Gaussian with mean and variance estimated from the data.
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.
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.
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
tol : float, optional
The relative increment in the results before declaring convergence.
verbose : boolean, optional
Verbosity mode.
random_state : integer or numpy.RandomState, optional
The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
copy_x : boolean, optional
When precomputing distances it is more numerically accurate to center the data first. If copy_x is True, 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.
n_jobs : int
The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel.
If 1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below 1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = 2, all CPUs but one are used.
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.