8.1.7. sklearn.cluster.Ward

class sklearn.cluster.Ward(n_clusters=2, memory=Memory(cachedir=None), connectivity=None, copy=True, n_components=None)

Ward hierarchical clustering: constructs a tree and cuts it.

Parameters :

n_clusters : int or ndarray

The number of clusters to find.

connectivity : sparse matrix.

Connectivity matrix. Defines for each sample the neigbhoring samples following a given structure of the data. Default is None, i.e, the hiearchical clustering algorithm is unstructured.

memory : Instance of joblib.Memory or string

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.

copy : bool

Copy the connectivity matrix or work inplace.

n_components : int (optional)

The number of connected components in the graph defined by the connectivity matrix. If not set, it is estimated.

Attributes

children_	array-like, shape = [n_nodes, 2]	List of the children of each nodes. Leaves of the tree do not appear.
labels_	array [n_points]	cluster labels for each point
n_leaves_	int	Number of leaves in the hiearchical tree.

Methods

fit(X)	Fit the hierarchical clustering on the data
set_params(**params)	Set the parameters of the estimator.

__init__(n_clusters=2, memory=Memory(cachedir=None), connectivity=None, copy=True, n_components=None)

fit(X)

Fit the hierarchical clustering on the data

Parameters :

X : array-like, shape = [n_samples, n_features]

The samples a.k.a. observations.

Returns :

self :

set_params(**params)

Set the parameters of the 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 :