K-means Clustering

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

Documentation

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.

• 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:

  ‘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 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.

• algorithm

  K-means algorithm to use. The classical EM-style algorithm is “full”. The “elkan” variation is more efficient on data 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).

  For now “auto” (kept for backward compatibiliy) chooses “elkan” but it might change in the future for a better heuristic.

  Changed in version 0.18: Added Elkan algorithm

• max_iter

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

• tol

  Relative tolerance with regards to Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence.

• 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:

modelmodel

Model

Definition

Input ports

Output ports

model

model

Model

class node_clustering.KMeansClustering