cratepy.clustering.clusteringalgs.KMeansSK

class KMeansSK(init='k-means++', n_init=10, max_iter=300, tol=0.0001, random_state=None, algorithm='lloyd', n_clusters=None)

Bases: ClusteringAlgorithm

K-Means clustering algorithm (wrapper).

Documentation: see here.

perform_clustering(self, data_matrix):

Perform cluster analysis and get cluster label of each dataset item.

Constructor.

Parameters:

• n_clusters (int, default=None) – Number of clusters to find.

• init ({‘k-means++’, ‘random’, ndarray, callable}, default=’k-means++’) – Method for centroid initialization.

• n_init (int, default=10) – Number of times K-Means is run with different centroid seeds.

• max_iter (int, default=300) – Maximum number of iterations.

• tol (float, default=1e-4) – Convergence tolerance (based on Frobenius norm of the different in the cluster centers of two consecutive iterations).

• random_state ({int, RandomState}, default=None) – Determines random number generation for centroid initialization. Use an int to make the randomness deterministic.

• algorithm ({'lloyd', 'elkan'}, default='lloyd') – K-Means algorithm to use. ‘lloyd’ is the classical EM-style algorithm, ‘elkan’ uses the triangle inequality to speed up convergence.

List of Public Methods

perform_clustering

Perform cluster analysis and get cluster label of each dataset item.

Methods

__init__(init='k-means++', n_init=10, max_iter=300, tol=0.0001, random_state=None, algorithm='lloyd', n_clusters=None)

Constructor.

Parameters:

• n_clusters (int, default=None) – Number of clusters to find.

• init ({‘k-means++’, ‘random’, ndarray, callable}, default=’k-means++’) – Method for centroid initialization.

• n_init (int, default=10) – Number of times K-Means is run with different centroid seeds.

• max_iter (int, default=300) – Maximum number of iterations.

• tol (float, default=1e-4) – Convergence tolerance (based on Frobenius norm of the different in the cluster centers of two consecutive iterations).

• random_state ({int, RandomState}, default=None) – Determines random number generation for centroid initialization. Use an int to make the randomness deterministic.

• algorithm ({'lloyd', 'elkan'}, default='lloyd') – K-Means algorithm to use. ‘lloyd’ is the classical EM-style algorithm, ‘elkan’ uses the triangle inequality to speed up convergence.

perform_clustering(data_matrix)

Perform cluster analysis and get cluster label of each dataset item.

Parameters:

data_matrix (numpy.ndarray (2d)) – Data matrix containing the required data to perform the cluster analysis (numpy.ndarray of shape (n_items, n_features)).

Returns:

cluster_labels – Cluster label (int) assigned to each dataset item.

Return type:

numpy.ndarray (1d)