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[MRG+1] Refactor birch-documentation #8298

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MechCoder merged 4 commits into from
Feb 7, 2017
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[MRG+1] Refactor birch-documentation #8298

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Refactor birch-documentation
MechCoder Feb 5, 2017
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49 changes: 35 additions & 14 deletions sklearn/cluster/birch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -324,10 +324,11 @@ def radius(self):
class Birch(BaseEstimator, TransformerMixin, ClusterMixin):
"""Implements the Birch clustering algorithm.

Every new sample is inserted into the root of the Clustering Feature
Tree. It is then clubbed together with the subcluster that has the
centroid closest to the new sample. This is done recursively till it
ends up at the subcluster of the leaf of the tree has the closest centroid.
It is a memory-efficient, online-learning algorithm provided as an
alternative to :class:`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 :class:`AgglomerativeClustering`.

Read more in the :ref:`User Guide <birch>`.

Expand All @@ -336,23 +337,29 @@ class Birch(BaseEstimator, TransformerMixin, ClusterMixin):
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.
subcluster is started. Setting this value to be very low promotes
splitting and vice-versa.

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
the node has to be split. The corresponding parent also has to be
split and if the number of subclusters in the parent is greater than
the branching factor, then it has to be split recursively.
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. If None, this final
clustering step is not performed and the subclusters are returned
as they are. 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. If an int is provided, the model
fit is AgglomerativeClustering with n_clusters set to the int.
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 :class:`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.
Expand Down Expand Up @@ -402,6 +409,20 @@ class Birch(BaseEstimator, TransformerMixin, ClusterMixin):
* Roberto Perdisci
JBirch - Java implementation of BIRCH clustering algorithm
https://code.google.com/archive/p/jbirch

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.
"""

def __init__(self, threshold=0.5, branching_factor=50, n_clusters=3,
Expand Down