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, compute_distances=False) [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 if distance_threshold is not None.  
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.  
connectivityarray-like 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 if distance_threshold is not None. By default compute_full_tree is “auto”, which is equivalent to True when distance_threshold is not None or that n_clusters is inferior to the maximum between 100 or 0.02 * n_samples. Otherwise, “auto” is equivalent to False.  
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 be None and compute_full_tree must be True.  New in version 0.21.   
compute_distancesbool, default=False 

Computes distances between clusters even if distance_threshold is not used. This can be used to make dendrogram visualization, but introduces a computational and memory overhead.  New in version 0.24.     Attributes 
 
n_clusters_int 

The number of clusters found by the algorithm. If distance_threshold=None, it will be equal to the given n_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 replace n_components_.   
children_array-like of shape (n_samples-1, 2) 

The children of each non-leaf node. Values less than n_samples correspond to leaves of the tree which are the original samples. A node i greater than or equal to n_samples is a non-leaf node and has children children_[i - n_samples]. Alternatively at the i-th iteration, children[i][0] and children[i][1] are merged to form node n_samples + i  
distances_array-like of shape (n_nodes-1,) 

Distances between nodes in the corresponding place in children_. Only computed if distance_threshold is used or compute_distances is set to True.     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.    
fit(X, y=None) [source]
 
Fit the hierarchical clustering from features, or distance matrix.  Parameters 
 
Xarray-like, 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 
 
Xarray-like of 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 of 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 
 
paramsdict 

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 Pipeline). 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 
 
selfestimator instance 

Estimator instance.     
 
 Examples using sklearn.cluster.AgglomerativeClustering
 
  Plot Hierarchical Clustering Dendrogram  

  Agglomerative clustering with and without structure  

  Various Agglomerative Clustering on a 2D embedding of digits  

  A demo of structured Ward hierarchical clustering on an image of coins  

  Hierarchical clustering: structured vs unstructured ward  

  Agglomerative clustering with different metrics  

  Inductive Clustering  

  Comparing different hierarchical linkage methods on toy datasets  

  Comparing different clustering algorithms on toy datasets