`sklearn.cluster`.Birch¶

class sklearn.cluster.Birch(threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True)[source]¶

Implements the Birch clustering algorithm.

It is a memory-efficient, online-learning algorithm provided as an alternative to MiniBatchKMeans. It constructs a tree data structure with the cluster centroids being read off the leaf. These can be either the final cluster centroids or can be provided as input to another clustering algorithm such as AgglomerativeClustering.

Read more in the User Guide.

Parameters

thresholdfloat, default 0.5

The radius of the subcluster obtained by merging a new sample and the closest subcluster should be lesser than the threshold. Otherwise a new subcluster is started. Setting this value to be very low promotes splitting and vice-versa.

branching_factorint, default 50

Maximum number of CF subclusters in each node. If a new samples enters such that the number of subclusters exceed the branching_factor then that node is split into two nodes with the subclusters redistributed in each. The parent subcluster of that node is removed and two new subclusters are added as parents of the 2 split nodes.

n_clustersint, instance of sklearn.cluster model, default 3

Number of clusters after the final clustering step, which treats the subclusters from the leaves as new samples.

None : the final clustering step is not performed and the subclusters are returned as they are.
sklearn.cluster Estimator : If a model is provided, the model is fit treating the subclusters as new samples and the initial data is mapped to the label of the closest subcluster.
int : the model fit is AgglomerativeClustering with n_clusters set to be equal to the int.

compute_labelsbool, default True

Whether or not to compute labels for each fit.

copybool, default True

Whether or not to make a copy of the given data. If set to False, the initial data will be overwritten.

Attributes

root__CFNode: Root of the CFTree.
dummy_leaf__CFNode: Start pointer to all the leaves.
subcluster_centers_ndarray,: Centroids of all subclusters read directly from the leaves.
subcluster_labels_ndarray,: Labels assigned to the centroids of the subclusters after they are clustered globally.
labels_ndarray, shape (n_samples,): Array of labels assigned to the input data. if partial_fit is used instead of fit, they are assigned to the last batch of data.

Notes

The tree data structure consists of nodes with each node consisting of a number of subclusters. The maximum number of subclusters in a node is determined by the branching factor. Each subcluster maintains a linear sum, squared sum and the number of samples in that subcluster. In addition, each subcluster can also have a node as its child, if the subcluster is not a member of a leaf node.

For a new point entering the root, it is merged with the subcluster closest to it and the linear sum, squared sum and the number of samples of that subcluster are updated. This is done recursively till the properties of the leaf node are updated.

References

Tian Zhang, Raghu Ramakrishnan, Maron Livny BIRCH: An efficient data clustering method for large databases. https://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf
Roberto Perdisci JBirch - Java implementation of BIRCH clustering algorithm https://code.google.com/archive/p/jbirch

Examples

>>> from sklearn.cluster import Birch
>>> X = [[0, 1], [0.3, 1], [-0.3, 1], [0, -1], [0.3, -1], [-0.3, -1]]
>>> brc = Birch(n_clusters=None)
>>> brc.fit(X)
Birch(n_clusters=None)
>>> brc.predict(X)
array([0, 0, 0, 1, 1, 1])

Methods

`fit`(self, X[, y])	Build a CF Tree for the input data.
`fit_predict`(self, X[, y])	Performs clustering on X and returns cluster labels.
`fit_transform`(self, X[, y])	Fit to data, then transform it.
`get_params`(self[, deep])	Get parameters for this estimator.
`partial_fit`(self[, X, y])	Online learning.
`predict`(self, X)	Predict data using the `centroids_` of subclusters.
`set_params`(self, \\params)	Set the parameters of this estimator.
`transform`(self, X)	Transform X into subcluster centroids dimension.

__init__(self, threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y=None)[source]¶

Build a CF Tree for the input data.

Parameters

X{array-like, sparse matrix}, shape (n_samples, n_features): Input data.
yIgnored

fit_predict(self, X, y=None)[source]¶

Performs clustering on X and returns cluster labels.

Parameters

Xndarray, shape (n_samples, n_features): Input data.
yIgnored: not used, present for API consistency by convention.

Returns

labelsndarray, shape (n_samples,): cluster labels

fit_transform(self, X, y=None, **fit_params)[source]¶

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Xnumpy array of shape [n_samples, n_features]: Training set.
ynumpy array of shape [n_samples]: Target values.

Returns

X_newnumpy array of shape [n_samples, n_features_new]: Transformed array.

get_params(self, deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepboolean, optional: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any: Parameter names mapped to their values.

partial_fit(self, X=None, y=None)[source]¶

Online learning. Prevents rebuilding of CFTree from scratch.

Parameters

X{array-like, sparse matrix}, shape (n_samples, n_features), None: Input data. If X is not provided, only the global clustering step is done.
yIgnored

predict(self, X)[source]¶

Predict data using the centroids_ of subclusters.

Avoid computation of the row norms of X.

Parameters

X{array-like, sparse matrix}, shape (n_samples, n_features): Input data.

Returns

labelsndarray, shape(n_samples): Labelled data.

set_params(self, **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

transform(self, X)[source]¶

Transform X into subcluster centroids dimension.

Each dimension represents the distance from the sample point to each cluster centroid.

Parameters

X{array-like, sparse matrix}, shape (n_samples, n_features): Input data.

Returns

X_trans{array-like, sparse matrix}, shape (n_samples, n_clusters): Transformed data.

sklearn.cluster.Birch¶

`sklearn.cluster`.Birch¶