6.8.1. scikits.learn.cluster.KMeans

class scikits.learn.cluster.KMeans(k=8, init='random', n_init=10, max_iter=300, tol=0.0001, verbose=0, rng=None, copy_x=True)

K-Means clustering

Parameters :

data : ndarray

A M by N array of M observations in N dimensions or a length M array of M one-dimensional observations.

k : int or ndarray

The number of clusters to form as well as the number of centroids to generate. If init initialization string is ‘matrix’, or if a ndarray is given instead, it is interpreted as initial cluster to use instead.

max_iter : int

Maximum number of iterations of the k-means algorithm for a single 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’, ‘points’, ‘matrix’}

Method for initialization, defaults to ‘random’:

‘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.

‘points’: choose k observations (rows) at random from data for the initial centroids.

‘matrix’: interpret the k parameter as a k by M (or length k array for one-dimensional data) array of initial centroids.

tol: float, optional default: 1e-4 :

Relative tolerance w.r.t. inertia to declare convergence

Notes

The k-means problem is solved using the Lloyd 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.

Attributes

cluster_centers_: array, [n_clusters, n_features]	Coordinates of cluster centers
labels_:	Labels of each point
inertia_: float	The value of the inertia criterion associated with the chosen partition.

Methods

fit(X):

Compute K-Means clustering

__init__(k=8, init='random', n_init=10, max_iter=300, tol=0.0001, verbose=0, rng=None, copy_x=True)

fit(X, **params)

Compute k-means