Python 非監督式機器學習: 距離導向聚類法(k-Means 演算法); 使用 scikit-learn ; 學生分群 ; from sklearn.cluster import KMeans

Init signature:
KMeans(
 n_clusters=8,
 *,
 init=’k-means++’,
 n_init=’auto’,
 max_iter=300,
 tol=0.0001,
 verbose=0,
 random_state=None,
 copy_x=True,
 algorithm=’lloyd’,
)
Docstring:
K-Means clustering.

Read more in the :ref:`User Guide <k_means>`.

Parameters
———-

n_clusters : int, default=8
The number of clusters to form as well as the number of
centroids to generate.

For an example of how to choose an optimal value for `n_clusters` refer to
:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`.

init : {‘k-means++’, ‘random’}, callable or array-like of shape (n_clusters, n_features), default=’k-means++’
Method for initialization:

* ‘k-means++’ : selects initial cluster centroids using sampling based on an empirical probability distribution of the points’ contribution to the overall inertia. This technique speeds up convergence. The algorithm implemented is “greedy k-means++”. It differs from the vanilla k-means++ by making several trials at each sampling step and choosing the best centroid among them.

* ‘random’: choose `n_clusters` observations (rows) at random from data for the initial centroids.

* If an array 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, n_clusters and a random state and return an initialization.

For an example of how to use the different `init` strategies, see
:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`.

For an evaluation of the impact of initialization, see the example
:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`.

n_init : ‘auto’ or int, default=’auto’
Number of times the k-means algorithm is run with different centroid
seeds. The final results is the best output of `n_init` consecutive runs
in terms of inertia. Several runs are recommended for sparse
high-dimensional problems (see :ref:`kmeans_sparse_high_dim`).

When `n_init=’auto’`, the number of runs depends on the value of init:
10 if using `init=’random’` or `init` is a callable;
1 if using `init=’k-means++’` or `init` is an array-like.

.. versionadded:: 1.2
Added ‘auto’ option for `n_init`.

.. versionchanged:: 1.4
Default value for `n_init` changed to `’auto’`.

max_iter : int, default=300
Maximum number of iterations of the k-means algorithm for a
single run.

tol : float, default=1e-4
Relative tolerance with regards to Frobenius norm of the difference
in the cluster centers of two consecutive iterations to declare
convergence.

verbose : int, default=0
Verbosity mode.

random_state : int, RandomState instance or None, default=None
Determines random number generation for centroid initialization. Use
an int to make the randomness deterministic.
See :term:`Glossary <random_state>`.

copy_x : bool, default=True
When pre-computing distances it is more numerically accurate to center
the data first. If copy_x is True (default), 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. Note that if
the original data is not C-contiguous, a copy will be made even if
copy_x is False. If the original data is sparse, but not in CSR format,
a copy will be made even if copy_x is False.

algorithm : {“lloyd”, “elkan”}, default=”lloyd”
K-means algorithm to use. The classical EM-style algorithm is `”lloyd”`.
The `”elkan”` variation can be more efficient on some datasets with
well-defined clusters, by using the triangle inequality. However it’s
more memory intensive due to the allocation of an extra array of shape
`(n_samples, n_clusters)`.

.. versionchanged:: 0.18
Added Elkan algorithm

.. versionchanged:: 1.1
Renamed “full” to “lloyd”, and deprecated “auto” and “full”.
Changed “auto” to use “lloyd” instead of “elkan”.

Attributes
———-
cluster_centers_ : ndarray of shape (n_clusters, n_features)
Coordinates of cluster centers. If the algorithm stops before fully
converging (see “tol“ and “max_iter“), these will not be
consistent with “labels_“.

labels_ : ndarray of shape (n_samples,)
Labels of each point

inertia_ : float
Sum of squared distances of samples to their closest cluster center,
weighted by the sample weights if provided.

n_iter_ : int
Number of iterations run.

n_features_in_ : int
Number of features seen during :term:`fit`.

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.

.. versionadded:: 1.0

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 than the default batch implementation.

Notes
—–
The k-means problem is solved using either Lloyd’s or Elkan’s algorithm.

The average complexity is given by O(k n T), where 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.
Refer to :doi:`”How slow is the k-means method?” D. Arthur and S. Vassilvitskii –
SoCG2006.<10.1145/1137856.1137880>` for more details.

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.

If the algorithm stops before fully converging (because of “tol“ or
“max_iter“), “labels_“ and “cluster_centers_“ will not be consistent,
i.e. the “cluster_centers_“ will not be the means of the points in each
cluster. Also, the estimator will reassign “labels_“ after the last
iteration to make “labels_“ consistent with “predict“ on the training
set.

Examples
——–

>>> from sklearn.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
… [10, 2], [10, 4], [10, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0, n_init=”auto”).fit(X)
>>> kmeans.labels_
array([1, 1, 1, 0, 0, 0], dtype=int32)
>>> kmeans.predict([[0, 0], [12, 3]])
array([1, 0], dtype=int32)
>>> kmeans.cluster_centers_
array([[10., 2.],
[ 1., 2.]])

For examples of common problems with K-Means and how to address them see
:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py`.

For a demonstration of how K-Means can be used to cluster text documents see
:ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`.

For a comparison between K-Means and MiniBatchKMeans refer to example
:ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py`.

For a comparison between K-Means and BisectingKMeans refer to example
:ref:`sphx_glr_auto_examples_cluster_plot_bisect_kmeans.py`.
File: c:\users\iec120639\appdata\local\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py
Type: ABCMeta
Subclasses: