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def linkage(y, method='single', metric='euclidean', optimal_ordering=False)
Perform hierarchical/agglomerative clustering.
The input y may be either a 1-D condensed distance matrix
or a 2-D array of observation vectors.
If y is a 1-D condensed distance matrix,
then y must be a :math:`\binom{n}{2}` sized
vector, where n is the number of original observations paired
in the distance matrix. The behavior of this function is very
similar to the MATLAB linkage function.
A :math:`(n-1)` by 4 matrix ``Z`` is returned. At the
:math:`i`-th iteration, clusters with indices ``Z[i, 0]`` and
``Z[i, 1]`` are combined to form cluster :math:`n + i`. A
cluster with an index less than :math:`n` corresponds to one of
the :math:`n` original observations. The distance between
clusters ``Z[i, 0]`` and ``Z[i, 1]`` is given by ``Z[i, 2]``. The
fourth value ``Z[i, 3]`` represents the number of original
observations in the newly formed cluster.
The following linkage methods are used to compute the distance
:math:`d(s, t)` between two clusters :math:`s` and
:math:`t`. The algorithm begins with a forest of clusters that
have yet to be used in the hierarchy being formed. When two
clusters :math:`s` and :math:`t` from this forest are combined
into a single cluster :math:`u`, :math:`s` and :math:`t` are
removed from the forest, and :math:`u` is added to the
forest. When only one cluster remains in the forest, the algorithm
stops, and this cluster becomes the root.
A distance matrix is maintained at each iteration. The ``d[i,j]``
entry corresponds to the distance between cluster :math:`i` and
:math:`j` in the original forest.
At each iteration, the algorithm must update the distance matrix
to reflect the distance of the newly formed cluster u with the
remaining clusters in the forest.
Suppose there are :math:`|u|` original observations
:math:`u[0], \ldots, u[|u|-1]` in cluster :math:`u` and
:math:`|v|` original objects :math:`v[0], \ldots, v[|v|-1]` in
cluster :math:`v`. Recall, :math:`s` and :math:`t` are
combined to form cluster :math:`u`. Let :math:`v` be any
remaining cluster in the forest that is not :math:`u`.
The following are methods for calculating the distance between the
newly formed cluster :math:`u` and each :math:`v`.
* method='single' assigns
.. math::
d(u,v) = \min(dist(u[i],v[j]))
for all points :math:`i` in cluster :math:`u` and
:math:`j` in cluster :math:`v`. This is also known as the
Nearest Point Algorithm.
* method='complete' assigns
.. math::
d(u, v) = \max(dist(u[i],v[j]))
for all points :math:`i` in cluster u and :math:`j` in
cluster :math:`v`. This is also known by the Farthest Point
Algorithm or Voor Hees Algorithm.
* method='average' assigns
.. math::
d(u,v) = \sum_{ij} \frac{d(u[i], v[j])}
{(|u|*|v|)}
for all points :math:`i` and :math:`j` where :math:`|u|`
and :math:`|v|` are the cardinalities of clusters :math:`u`
and :math:`v`, respectively. This is also called the UPGMA
algorithm.
* method='weighted' assigns
.. math::
d(u,v) = (dist(s,v) + dist(t,v))/2
where cluster u was formed with cluster s and t and v
is a remaining cluster in the forest (also called WPGMA).
* method='centroid' assigns
.. math::
dist(s,t) = ||c_s-c_t||_2
where :math:`c_s` and :math:`c_t` are the centroids of
clusters :math:`s` and :math:`t`, respectively. When two
clusters :math:`s` and :math:`t` are combined into a new
cluster :math:`u`, the new centroid is computed over all the
original objects in clusters :math:`s` and :math:`t`. The
distance then becomes the Euclidean distance between the
centroid of :math:`u` and the centroid of a remaining cluster
:math:`v` in the forest. This is also known as the UPGMC
algorithm.
* method='median' assigns :math:`d(s,t)` like the ``centroid``
method. When two clusters :math:`s` and :math:`t` are combined
into a new cluster :math:`u`, the average of centroids s and t
give the new centroid :math:`u`. This is also known as the
WPGMC algorithm.
* method='ward' uses the Ward variance minimization algorithm.
The new entry :math:`d(u,v)` is computed as follows,
.. math::
d(u,v) = \sqrt{\frac{|v|+|s|}
{T}d(v,s)^2
+ \frac{|v|+|t|}
{T}d(v,t)^2
- \frac{|v|}
{T}d(s,t)^2}
where :math:`u` is the newly joined cluster consisting of
clusters :math:`s` and :math:`t`, :math:`v` is an unused
cluster in the forest, :math:`T=|v|+|s|+|t|`, and
:math:`|*|` is the cardinality of its argument. This is also
known as the incremental algorithm.
Warning: When the minimum distance pair in the forest is chosen, there
may be two or more pairs with the same minimum distance. This
implementation may choose a different minimum than the MATLAB
version.
Parameters
----------
y : ndarray
A condensed distance matrix. A condensed distance matrix
is a flat array containing the upper triangular of the distance matrix.
This is the form that ``pdist`` returns. Alternatively, a collection of
:math:`m` observation vectors in :math:`n` dimensions may be passed as
an :math:`m` by :math:`n` array. All elements of the condensed distance
matrix must be finite, i.e., no NaNs or infs.
method : str, optional
The linkage algorithm to use. See the ``Linkage Methods`` section below
for full descriptions.
metric : str or function, optional
The distance metric to use in the case that y is a collection of
observation vectors; ignored otherwise. See the ``pdist``
function for a list of valid distance metrics. A custom distance
function can also be used.
optimal_ordering : bool, optional
If True, the linkage matrix will be reordered so that the distance
between successive leaves is minimal. This results in a more intuitive
tree structure when the data are visualized. defaults to False, because
this algorithm can be slow, particularly on large datasets [2]_. See
also the `optimal_leaf_ordering` function.
.. versionadded:: 1.0.0
Returns
-------
Z : ndarray
The hierarchical clustering encoded as a linkage matrix.
Notes
-----
1. For method 'single', an optimized algorithm based on minimum spanning
tree is implemented. It has time complexity :math:`O(n^2)`.
For methods 'complete', 'average', 'weighted' and 'ward', an algorithm
called nearest-neighbors chain is implemented. It also has time
complexity :math:`O(n^2)`.
For other methods, a naive algorithm is implemented with :math:`O(n^3)`
time complexity.
All algorithms use :math:`O(n^2)` memory.
Refer to [1]_ for details about the algorithms.
2. Methods 'centroid', 'median', and 'ward' are correctly defined only if
Euclidean pairwise metric is used. If `y` is passed as precomputed
pairwise distances, then it is the user's responsibility to assure that
these distances are in fact Euclidean, otherwise the produced result
will be incorrect.
See Also
--------
scipy.spatial.distance.pdist : pairwise distance metrics
References
----------
.. [1] Daniel Mullner, "Modern hierarchical, agglomerative clustering
algorithms", :arXiv:`1109.2378v1`.
.. [2] Ziv Bar-Joseph, David K. Gifford, Tommi S. Jaakkola, "Fast optimal
leaf ordering for hierarchical clustering", 2001. Bioinformatics
:doi:`10.1093/bioinformatics/17.suppl_1.S22`
Examples
--------
>>> from scipy.cluster.hierarchy import dendrogram, linkage
>>> from matplotlib import pyplot as plt
>>> X = [[i] for i in [2, 8, 0, 4, 1, 9, 9, 0]]
>>> Z = linkage(X, 'ward')
>>> fig = plt.figure(figsize=(25, 10))
>>> dn = dendrogram(Z)
>>> Z = linkage(X, 'single')
>>> fig = plt.figure(figsize=(25, 10))
>>> dn = dendrogram(Z)
>>> plt.show()
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