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.
Returns: self :