K-means Clustering

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Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

class node_clustering.KMeansClustering[source]

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