sklearn.cluster
.Ward¶

class
sklearn.cluster.
Ward
(n_clusters=2, memory=Memory(cachedir=None), connectivity=None, n_components=None, compute_full_tree='auto', pooling_func=<function mean at 0x1c58cf8>)[source]¶ Ward hierarchical clustering: constructs a tree and cuts it.
Recursively merges the pair of clusters that minimally increases withincluster variance.
Parameters: n_clusters : int or ndarray
The number of clusters to find.
connectivity : sparse matrix (optional)
Connectivity matrix. Defines for each sample the neighboring samples following a given structure of the data. Default is None, i.e, the hierarchical clustering algorithm is unstructured.
memory : Instance of joblib.Memory or string (optional)
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.
n_components : int (optional)
The number of connected components in the graph defined by the connectivity matrix. If not set, it is estimated.
compute_full_tree : bool or ‘auto’ (optional)
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.
Attributes: labels_ : array [n_features]
cluster labels for each feature
n_leaves_ : int
Number of leaves in the hierarchical tree.
n_components_ : int
The estimated number of connected components in the graph.
children_ : arraylike, shape (n_nodes1, 2)
The children of each nonleaf node. Values less than n_samples refer to leaves of the tree. A greater value i indicates a node with children children_[i  n_samples].
See also
AgglomerativeClustering
 agglomerative hierarchical clustering
Methods

__init__
(n_clusters=2, memory=Memory(cachedir=None), connectivity=None, n_components=None, compute_full_tree='auto', pooling_func=<function mean at 0x1c58cf8>)[source]¶

fit
(X, y=None)[source]¶ Fit the hierarchical clustering on the data
Parameters: X : arraylike, shape = [n_samples, n_features]
The samples a.k.a. observations.
Returns: self :

fit_predict
(X, y=None)[source]¶ Performs clustering on X and returns cluster labels.
Parameters: X : ndarray, shape (n_samples, n_features)
Input data.
Returns: y : ndarray, shape (n_samples,)
cluster labels

get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping 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 former have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: self :