sklearn.cluster.AgglomerativeClustering

class sklearn.cluster.AgglomerativeClustering(n_clusters=2, affinity='euclidean', memory=None, connectivity=None, compute_full_tree='auto', linkage='ward', pooling_func=<function mean>)[source]

Agglomerative Clustering

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

Read more in the User Guide.

Parameters:

n_clusters : int, default=2

The number of clusters to find.

connectivity : array-like or callable, optional

Connectivity matrix. Defines for each sample the neighboring samples following a given structure of the data. This can be a connectivity matrix itself or a callable that transforms the data into a connectivity matrix, such as derived from kneighbors_graph. 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 sklearn.externals.joblib.Memory or string, optional (default=None)

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.

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-1, 2)

The children of each non-leaf node. Values less than n_samples correspond to leaves of the tree which are the original samples. A node i greater than or equal to n_samples is a non-leaf node and has children children_[i - n_samples]. Alternatively at the i-th iteration, children[i][0] and children[i][1] are merged to form node n_samples + i

Methods

fit(X[, y]) 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=None, connectivity=None, compute_full_tree='auto', linkage='ward', pooling_func=<function mean>)[source]
fit(X, y=None)[source]

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)[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 latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :