DOC Better information about transductive and inductive cluster methods
@@ -51,62 +51,64 @@ Overview of clustering methods
- number of clusters
- Very large ``n_samples``, medium ``n_clusters`` with
:ref:`MiniBatch code <mini_batch_kmeans>`
- - General-purpose, even cluster size, flat geometry, not too many clusters
+ - General-purpose, even cluster size, flat geometry,
+ not too many clusters, inductive
- Distances between points
* - :ref:`Affinity propagation <affinity_propagation>`
- damping, sample preference
- Not scalable with n_samples
- - Many clusters, uneven cluster size, non-flat geometry
+ - Many clusters, uneven cluster size, non-flat geometry, inductive
- Graph distance (e.g. nearest-neighbor graph)
* - :ref:`Mean-shift <mean_shift>`
- bandwidth
- Not scalable with ``n_samples``
- - Many clusters, uneven cluster size, non-flat geometry
+ - Many clusters, uneven cluster size, non-flat geometry, inductive
- Distances between points
* - :ref:`Spectral clustering <spectral_clustering>`
- number of clusters
- Medium ``n_samples``, small ``n_clusters``
- - Few clusters, even cluster size, non-flat geometry
+ - Few clusters, even cluster size, non-flat geometry, transductive
- Graph distance (e.g. nearest-neighbor graph)
* - :ref:`Ward hierarchical clustering <hierarchical_clustering>`
- number of clusters or distance threshold
- Large ``n_samples`` and ``n_clusters``
- - Many clusters, possibly connectivity constraints
+ - Many clusters, possibly connectivity constraints, transductive
- Distances between points
* - :ref:`Agglomerative clustering <hierarchical_clustering>`
- number of clusters or distance threshold, linkage type, distance
- Large ``n_samples`` and ``n_clusters``
- Many clusters, possibly connectivity constraints, non Euclidean
- distances
+ distances, transductive
- Any pairwise distance
* - :ref:`DBSCAN <dbscan>`
- neighborhood size
- Very large ``n_samples``, medium ``n_clusters``
- - Non-flat geometry, uneven cluster sizes
+ - Non-flat geometry, uneven cluster sizes, transductive
- Distances between nearest points
* - :ref:`OPTICS <optics>`
- minimum cluster membership
- Very large ``n_samples``, large ``n_clusters``
- - Non-flat geometry, uneven cluster sizes, variable cluster density
+ - Non-flat geometry, uneven cluster sizes, variable cluster density,
+ transductive
- Distances between points
* - :ref:`Gaussian mixtures <mixture>`
- many
- Not scalable
- - Flat geometry, good for density estimation
+ - Flat geometry, good for density estimation, inductive
- Mahalanobis distances to centers
* - :ref:`BIRCH <birch>`
- branching factor, threshold, optional global clusterer.
- Large ``n_clusters`` and ``n_samples``
- - Large dataset, outlier removal, data reduction.
+ - Large dataset, outlier removal, data reduction, inductive
- Euclidean distance between points
Non-flat geometry clustering is useful when the clusters have a specific
@@ -119,6 +121,10 @@ Gaussian mixture models, useful for clustering, are described in
mixture models. KMeans can be seen as a special case of Gaussian mixture
model with equal covariance per component.
+:term:`Transductive <transductive>` clustering methods (in contrast to
+:term:`inductive` clustering methods) are not designed to be applied to new,
+unseen data.
+
.. _k_means:
K-means
