.. _`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.

    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*

    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`.




:Attributes:


  - *cluster_centers_*

    Coordinates of cluster centers


  - *labels_*

    Labels of each point


  - *inertia_*

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




:Inputs:


:Outputs:
    **model** : model
        Model

*Ports*:

    **Outputs**:

        :model: model

            Model

*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.

        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**
        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`.

.. automodule:: node_clustering

.. class:: KMeansClustering