sklearn.datasets.make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None)[source]

Generate isotropic Gaussian blobs for clustering.


n_samples : int, optional (default=100)

The total number of points equally divided among clusters.

n_features : int, optional (default=2)

The number of features for each sample.

centers : int or array of shape [n_centers, n_features], optional

(default=3) The number of centers to generate, or the fixed center locations.

cluster_std: float or sequence of floats, optional (default=1.0) :

The standard deviation of the clusters.

center_box: pair of floats (min, max), optional (default=(-10.0, 10.0)) :

The bounding box for each cluster center when centers are generated at random.

shuffle : boolean, optional (default=True)

Shuffle the samples.

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.


X : array of shape [n_samples, n_features]

The generated samples.

y : array of shape [n_samples]

The integer labels for cluster membership of each sample.

See also

a more intricate variant


>>> from sklearn.datasets.samples_generator import make_blobs
>>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,
...                   random_state=0)
>>> print(X.shape)
(10, 2)
>>> y
array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])

Examples using sklearn.datasets.make_blobs

Reference: Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages Between Data Poin...

Finds core samples of high density and expands clusters from them.

This example aims at showing characteristics of different clustering algorithms on datasets tha...

We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is fa...

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