heat.cluster.kmedoids

Module Implementing the Kmedoids Algorithm

Module Contents

class KMedoids(n_clusters: int = 8, init: str | heat.core.dndarray.DNDarray = 'random', max_iter: int = 300, random_state: int = None)

Bases: heat.cluster._kcluster._KCluster

Kmedoids with the Manhattan distance as fixed metric, calculating the median of the assigned cluster points as new cluster center and snapping the centroid to the the nearest datapoint afterwards. This is not the original implementation of k-medoids using PAM as originally proposed by in [1].

Parameters:

• n_clusters (int, optional, default: 8) – The number of clusters to form as well as the number of centroids to generate.

• init (str or DNDarray, default: ‘random’) –

  Method for initialization:

  • ‘k-medoids++’ : selects initial cluster centers for the clustering in a smart way to speed up convergence [2].

  • ‘random’: choose k observations (rows) at random from data for the initial centroids.

  • DNDarray: gives the initial centers, should be of Shape = (n_clusters, n_features)

• max_iter (int, default: 300) – Maximum number of iterations of the algorithm for a single run.

• random_state (int) – Determines random number generation for centroid initialization.

References

[1] Kaufman, L. and Rousseeuw, P.J. (1987), Clustering by means of Medoids, in Statistical Data Analysis Based on the L1 Norm and Related Methods, edited by Y. Dodge, North-Holland, 405416.

_update_centroids(x: heat.core.dndarray.DNDarray, matching_centroids: heat.core.dndarray.DNDarray)

Compute new centroid ci as closest sample to the median of the data points in x that are assigned to ci

Parameters:

• x (DNDarray) – Input data

• matching_centroids (DNDarray) – Array filled with indeces i indicating to which cluster ci each sample point in x is assigned

fit(x: heat.core.dndarray.DNDarray, oversampling: float = 2, iter_multiplier: float = 1)

Computes the centroid of a k-medoids clustering.

Parameters:

• x (DNDarray) – Training instances to cluster. Shape = (n_samples, n_features)

• oversampling (float) – oversampling factor used in the k-means|| initializiation of centroids

• iter_multiplier (float) – factor that increases the number of iterations used in the initialization of centroids