.. _`K-means Clustering`:

.. _`org.sysess.sympathy.machinelearning.k_means`:

K-means Clustering
~~~~~~~~~~~~~~~~~~

.. image:: dataset_blobs.svg
   :width: 48


Clusters data by trying to separate samples in n groups of equal variance

*Configuration*:


  - *n_clusters*

    The number of clusters to form as well as the number of
    centroids to generate.


  - *max_iter*

    Maximum number of iterations of the k-means algorithm for a
    single run.


  - *n_init*

    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*

    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.


  - *algorithm*

    K-means algorithm to use. The classical EM-style algorithm is "full".
    The "elkan" variation is more efficient by using the triangle
    inequality, but currently doesn't support sparse data. "auto" chooses
    "elkan" for dense data and "full" for sparse data.


  - *precompute_distances*

    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*

    Relative tolerance with regards to inertia to declare convergence


  - *n_jobs*

    The number of jobs to use for the computation. This works by computing
    each of the n_init runs in parallel.

    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
    ``-1`` means using all processors. See n_jobs
    for more details.


  - *random_state*

    Determines random number generation for centroid initialization. Use
    an int to make the randomness deterministic.
    See random_state.




*Attributes*:


  - *cluster_centers_*

    Coordinates of cluster centers. If the algorithm stops before fully
    converging (see ``tol`` and ``max_iter``), these will not be
    consistent with ``labels_``.


  - *labels_*

    Labels of each point


  - *inertia_*

    Sum of squared distances of samples to their closest cluster center.




*Input ports*:


*Output ports*:
    **model** : model
        Model



**n_clusters** (n_clusters)
    The number of clusters to form as well as the number of
    centroids to generate.
**max_iter** (max_iter)
    Maximum number of iterations of the k-means algorithm for a
    single run.
**n_init** (n_init)
    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** (init)
    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.
**algorithm** (algorithm)
    K-means algorithm to use. The classical EM-style algorithm is "full".
    The "elkan" variation is more efficient by using the triangle
    inequality, but currently doesn't support sparse data. "auto" chooses
    "elkan" for dense data and "full" for sparse data.
**precompute_distances** (precompute_distances)
    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** (tol)
    Relative tolerance with regards to inertia to declare convergence
**n_jobs** (n_jobs)
    The number of jobs to use for the computation. This works by computing
    each of the n_init runs in parallel.

    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
    ``-1`` means using all processors. See n_jobs
    for more details.
**random_state** (random_state)
    Determines random number generation for centroid initialization. Use
    an int to make the randomness deterministic.
    See random_state.

.. automodule:: node_clustering

.. class:: KMeansClustering