`sklearn.cluster`.AgglomerativeClustering¶

class sklearn.cluster.AgglomerativeClustering(n_clusters=2, affinity='euclidean', memory=Memory(cachedir=None), connectivity=None, n_components=None, compute_full_tree='auto', linkage='ward', pooling_func=<function mean at 0x2b3eef778320>)¶

Agglomerative Clustering

Recursively merges the pair of clusters that minimally increases a given linkage distance.

Parameters:

n_clusters : int, default=2

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.

affinity : string 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.

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.

linkage : {“ward”, “complete”, “average”}, optional, 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.

pooling_func : callable, default=np.mean

This combines the values of agglomerated features into a single value, and should accept an array of shape [M, N] and the keyword argument axis=1, and reduce it to an array of size [M].

Attributes:

`labels_` : array [n_samples]

cluster labels for each point

`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, affinity='euclidean', memory=Memory(cachedir=None), connectivity=None, n_components=None, compute_full_tree='auto', linkage='ward', pooling_func=<function mean at 0x2b3eef778320>)¶

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.