sklearn.cluster.KMeans¶
- class sklearn.cluster.KMeans(n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=1)[source]¶
K-Means clustering
Parameters: n_clusters : int, optional, default: 8
The number of clusters to form as well as the number of centroids to generate.
max_iter : int, default: 300
Maximum number of iterations of the k-means algorithm for a single run.
n_init : int, 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 an ndarray}
Method for initialization, defaults 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’: 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.
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, default: 1e-4
Relative tolerance with regards to inertia to declare convergence
n_jobs : int, default: 1
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.
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.
Attributes: cluster_centers_ : array, [n_clusters, n_features]
Coordinates of cluster centers
labels_ : :
Labels of each point
inertia_ : float
Sum of distances of samples to their closest cluster center.
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 to than the default batch implementation.
Notes
The k-means problem is solved using Lloyd’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.
Methods
fit(X[, y]) Compute k-means clustering. fit_predict(X) Compute cluster centers and predict cluster index for each sample. fit_transform(X[, y]) Compute clustering and transform X to cluster-distance space. get_params([deep]) Get parameters for this estimator. predict(X) Predict the closest cluster each sample in X belongs to. score(X) Opposite of the value of X on the K-means objective. set_params(**params) Set the parameters of this estimator. transform(X[, y]) Transform X to a cluster-distance space. - __init__(n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=1)[source]¶
- fit(X, y=None)[source]¶
Compute k-means clustering.
Parameters: X : array-like or sparse matrix, shape=(n_samples, n_features)
- fit_predict(X)[source]¶
Compute cluster centers and predict cluster index for each sample.
Convenience method; equivalent to calling fit(X) followed by predict(X).
- fit_transform(X, y=None)[source]¶
Compute clustering and transform X to cluster-distance space.
Equivalent to fit(X).transform(X), but more efficiently implemented.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
- predict(X)[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}, shape = [n_samples, n_features]
New data to predict.
Returns: labels : array, shape [n_samples,]
Index of the cluster each sample belongs to.
- score(X)[source]¶
Opposite of the value of X on the K-means objective.
Parameters: X : {array-like, sparse matrix}, shape = [n_samples, n_features]
New data.
Returns: score : float
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 pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :
- transform(X, y=None)[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}, shape = [n_samples, n_features]
New data to transform.
Returns: X_new : array, shape [n_samples, k]
X transformed in the new space.