sklearn.cluster.k_means

sklearn.cluster.k_means(X, n_clusters, init='k-means++', precompute_distances=True, n_init=10, max_iter=300, verbose=False, tol=0.0001, random_state=None, copy_x=True, n_jobs=1)

K-means clustering algorithm.

Parameters :

X : array-like 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 k-means algorithm to run.

n_init : int, optional, 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.

init : {‘k-means++’, ‘random’, or ndarray, or a callable}, optional

Method for initialization, default to ‘k-means++’:

‘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’: 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.

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 pre-computing 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 breaking down the pairwise matrix into n_jobs even slices and computing them 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.

Returns :

centroid : float ndarray with shape (k, n_features)

Centroids found at the last iteration of k-means.

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).