sklearn.cluster
.AgglomerativeClustering¶

class
sklearn.cluster.
AgglomerativeClustering
(n_clusters=2, *, affinity='euclidean', memory=None, connectivity=None, compute_full_tree='auto', linkage='ward', distance_threshold=None)[source]¶ Agglomerative Clustering
Recursively merges the pair of clusters that minimally increases a given linkage distance.
Read more in the User Guide.
 Parameters
 n_clustersint or None, default=2
The number of clusters to find. It must be
None
ifdistance_threshold
is notNone
. affinitystr or callable, default=’euclidean’
Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”, “manhattan”, “cosine”, or “precomputed”. If linkage is “ward”, only “euclidean” is accepted. If “precomputed”, a distance matrix (instead of a similarity matrix) is needed as input for the fit method.
 memorystr or object with the joblib.Memory interface, default=None
Used to cache the output of the computation of the tree. By default, no caching is done. If a string is given, it is the path to the caching directory.
 connectivityarraylike or callable, default=None
Connectivity matrix. Defines for each sample the neighboring samples following a given structure of the data. This can be a connectivity matrix itself or a callable that transforms the data into a connectivity matrix, such as derived from kneighbors_graph. Default is None, i.e, the hierarchical clustering algorithm is unstructured.
 compute_full_tree‘auto’ or bool, default=’auto’
Stop early the construction of the tree at n_clusters. This is useful to decrease computation time if the number of clusters is not small compared to the number of samples. This option is useful only when specifying a connectivity matrix. Note also that when varying the number of clusters and using caching, it may be advantageous to compute the full tree. It must be
True
ifdistance_threshold
is notNone
. By defaultcompute_full_tree
is “auto”, which is equivalent toTrue
whendistance_threshold
is notNone
or thatn_clusters
is inferior to the maximum between 100 or0.02 * n_samples
. Otherwise, “auto” is equivalent toFalse
. linkage{“ward”, “complete”, “average”, “single”}, default=”ward”
Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion.
ward minimizes the variance of the clusters being merged.
average uses the average of the distances of each observation of the two sets.
complete or maximum linkage uses the maximum distances between all observations of the two sets.
single uses the minimum of the distances between all observations of the two sets.
New in version 0.20: Added the ‘single’ option
 distance_thresholdfloat, default=None
The linkage distance threshold above which, clusters will not be merged. If not
None
,n_clusters
must beNone
andcompute_full_tree
must beTrue
.New in version 0.21.
 Attributes
 n_clusters_int
The number of clusters found by the algorithm. If
distance_threshold=None
, it will be equal to the givenn_clusters
. labels_ndarray of shape (n_samples)
cluster labels for each point
 n_leaves_int
Number of leaves in the hierarchical tree.
 n_connected_components_int
The estimated number of connected components in the graph.
New in version 0.21:
n_connected_components_
was added to replacen_components_
. children_arraylike of shape (n_samples1, 2)
The children of each nonleaf node. Values less than
n_samples
correspond to leaves of the tree which are the original samples. A nodei
greater than or equal ton_samples
is a nonleaf node and has childrenchildren_[i  n_samples]
. Alternatively at the ith iteration, children[i][0] and children[i][1] are merged to form noden_samples + i
Examples
>>> from sklearn.cluster import AgglomerativeClustering >>> import numpy as np >>> X = np.array([[1, 2], [1, 4], [1, 0], ... [4, 2], [4, 4], [4, 0]]) >>> clustering = AgglomerativeClustering().fit(X) >>> clustering AgglomerativeClustering() >>> clustering.labels_ array([1, 1, 1, 0, 0, 0])
Methods
fit
(X[, y])Fit the hierarchical clustering from features, or distance matrix.
fit_predict
(X[, y])Fit the hierarchical clustering from features or distance matrix, and return cluster labels.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.

__init__
(n_clusters=2, *, affinity='euclidean', memory=None, connectivity=None, compute_full_tree='auto', linkage='ward', distance_threshold=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(X, y=None)[source]¶ Fit the hierarchical clustering from features, or distance matrix.
 Parameters
 Xarraylike, shape (n_samples, n_features) or (n_samples, n_samples)
Training instances to cluster, or distances between instances if
affinity='precomputed'
. yIgnored
Not used, present here for API consistency by convention.
 Returns
 self

fit_predict
(X, y=None)[source]¶ Fit the hierarchical clustering from features or distance matrix, and return cluster labels.
 Parameters
 Xarraylike, shape (n_samples, n_features) or (n_samples, n_samples)
Training instances to cluster, or distances between instances if
affinity='precomputed'
. yIgnored
Not used, present here for API consistency by convention.
 Returns
 labelsndarray, shape (n_samples,)
Cluster labels.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.