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 0x1205578>)

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 0x1205578>)

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 :