class sklearn.cluster.AgglomerativeClustering(n_clusters=2, affinity=’euclidean’, memory=None, connectivity=None, compute_full_tree=’auto’, linkage=’ward’, pooling_func=<function mean>)
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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. 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 : None, str or object with the joblib.Memory interface, 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. 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. 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.
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 |
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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 |
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>)
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fit(X, y=None)
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Fit the hierarchical clustering on the data
Parameters: |
X : array-like, shape = [n_samples, n_features] The samples a.k.a. observations. y : Ignored |
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Returns: |
self : |
fit_predict(X, y=None)
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Performs clustering on X and returns cluster labels.
Parameters: |
X : ndarray, shape (n_samples, n_features) Input data. |
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Returns: |
y : ndarray, shape (n_samples,) cluster labels |
get_params(deep=True)
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Get parameters for this estimator.
Parameters: |
deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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Returns: |
params : mapping of string to any Parameter names mapped to their values. |
set_params(**params)
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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 : |
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sklearn.cluster.AgglomerativeClustering
© 2007–2017 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html