sklearn.cluster
.Birch¶

class
sklearn.cluster.
Birch
(threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True)¶ Implements the Birch clustering algorithm.
It is a memoryefficient, onlinelearning 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 asAgglomerativeClustering
.Read more in the User Guide.
Parameters: threshold : float, 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 viceversa.
branching_factor : int, 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_clusters : int, 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_labels : bool, default True
Whether or not to compute labels for each fit.
copy : bool, 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. http://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(branching_factor=50, n_clusters=None, threshold=0.5, ... compute_labels=True) >>> brc.fit(X) Birch(branching_factor=50, compute_labels=True, copy=True, n_clusters=None, threshold=0.5) >>> brc.predict(X) array([0, 0, 0, 1, 1, 1])
Methods
fit
(X[, y])Build a CF Tree for the input data. fit_predict
(X[, y])Performs clustering on X and returns cluster labels. fit_transform
(X[, y])Fit to data, then transform it. get_params
([deep])Get parameters for this estimator. partial_fit
([X, y])Online learning. predict
(X)Predict data using the centroids_
of subclusters.set_params
(**params)Set the parameters of this estimator. transform
(X)Transform X into subcluster centroids dimension. 
__init__
(threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True)¶

fit
(X, y=None)¶ Build a CF Tree for the input data.
Parameters: X : {arraylike, sparse matrix}, shape (n_samples, n_features)
Input data.

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

fit_transform
(X, y=None, **fit_params)¶ 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: X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns: X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.

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.

partial_fit
(X=None, y=None)¶ Online learning. Prevents rebuilding of CFTree from scratch.
Parameters: X : {arraylike, sparse matrix}, shape (n_samples, n_features), None
Input data. If X is not provided, only the global clustering step is done.

predict
(X)¶ Predict data using the
centroids_
of subclusters.Avoid computation of the row norms of X.
Parameters: X : {arraylike, sparse matrix}, shape (n_samples, n_features)
Input data.
Returns: labels : ndarray, shape(n_samples)
Labelled data.

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

transform
(X)¶ Transform X into subcluster centroids dimension.
Each dimension represents the distance from the sample point to each cluster centroid.
Parameters: X : {arraylike, sparse matrix}, shape (n_samples, n_features)
Input data.
Returns: X_trans : {arraylike, sparse matrix}, shape (n_samples, n_clusters)
Transformed data.