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Otros algoritmos de clustering

Scikit-Learn implementa un total de 9 algoritmos de clustering diferentes. En el siguiente gráfico se muestra su comportamiento en diferentes escenarios:

Algoritmos de clustering

Fuente: https://scikit-learn.org/stable/modules/clustering.html

E información sobre cada uno de ellos extraída de la misma página web:

Method name Parameters Scalability Usecase Geometry (metric used)
K-Means number of clusters Very large n_samples, medium n_clusters with MiniBatch code General-purpose, even cluster size, flat geometry, not too many clusters Distances between points
Affinity propagation damping, sample preference Not scalable with n_samples Many clusters, uneven cluster size, non-flat geometry Graph distance (e.g. nearest-neighbor graph)
Mean-shift bandwidth Not scalable with n_samples Many clusters, uneven cluster size, non-flat geometry Distances between points
Spectral clustering number of clusters Medium n_samples, small n_clusters Few clusters, even cluster size, non-flat geometry Graph distance (e.g. nearest-neighbor graph)
Ward hierarchical clustering number of clusters Large n_samples and n_clusters Many clusters, possibly connectivity constraints Distances between points
Agglomerative clustering number of clusters, linkage type, distance Large n_samples and n_clusters Many clusters, possibly connectivity constraints, non Euclidean distances Any pairwise distance
DBSCAN neighborhood size Very large n_samples, medium n_clusters Non-flat geometry, uneven cluster sizes Distances between nearest points
Gaussian mixtures many Not scalable Flat geometry, good for density estimation Mahalanobis distances to centers
Birch branching factor, threshold, optional global clusterer. Large n_clusters and n_samples Large dataset, outlier removal, data reduction. Euclidean distance between points