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sklearn.cluster.Ward

class sklearn.cluster.Ward(n_clusters=2, memory=Memory(cachedir=None), connectivity=None, copy=None, n_components=None, compute_full_tree='auto', pooling_func=<function mean at 0x29c4578>)

Ward hierarchical clustering: constructs a tree and cuts it.

Recursively merges the pair of clusters that minimally increases within-cluster 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.

See also

AgglomerativeClustering
agglomerative hierarchical clustering

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_ array-like, shape = [n_nodes, 2] The children of each non-leaf 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].

Methods

fit(X) Fit the hierarchical clustering on the data
fit_predict(X[, y]) Performs clustering on X and returns cluster labels.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
__init__(n_clusters=2, memory=Memory(cachedir=None), connectivity=None, copy=None, n_components=None, compute_full_tree='auto', pooling_func=<function mean at 0x29c4578>)
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 :

fit_predict(X, y=None)

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)

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)

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 :
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