fastcluster


Namefastcluster JSON
Version 1.2.6 PyPI version JSON
download
home_pagehttp://danifold.net
SummaryFast hierarchical clustering routines for R and Python.
upload_time2022-02-27 10:51:36
maintainer
docs_urlNone
authorDaniel Müllner
requires_python>=3
licenseBSD <http://opensource.org/licenses/BSD-2-Clause>
keywords dendrogram linkage cluster agglomerative hierarchical hierarchy ward
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            
This library provides Python functions for hierarchical clustering. It
generates hierarchical clusters from distance matrices or from vector data.

This module is intended to replace the functions
```
    linkage, single, complete, average, weighted, centroid, median, ward
```
in the module [`scipy.cluster.hierarchy`](
https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html) with the same
functionality but much faster algorithms. Moreover, the function
`linkage_vector` provides memory-efficient clustering for vector data.

The interface is very similar to MATLAB's Statistics Toolbox API to make code
easier to port from MATLAB to Python/NumPy. The core implementation of this
library is in C++ for efficiency.

**User manual:** [fastcluster.pdf](
https://github.com/dmuellner/fastcluster/raw/master/docs/fastcluster.pdf).

The “Yule” distance function changed in fastcluster version 1.2.0. This is
following a [change in SciPy 1.6.3](
https://github.com/scipy/scipy/commit/3b22d1da98dc1b5f64bc944c21f398d4ba782bce).
It is recommended to use fastcluster version 1.1.x together with SciPy versions
before 1.6.3 and fastcluster 1.2.x with SciPy ≥1.6.3.

The fastcluster package is considered stable and will undergo few changes
from now on. If some years from now there have not been any updates, this does
not necessarily mean that the package is unmaintained but maybe it just was
not necessary to correct anything. Of course, please still report potential
bugs and incompatibilities to daniel@danifold.net. You may also use
[my GitHub repository](https://github.com/dmuellner/fastcluster/)
for bug reports, pull requests etc.

Note that [PyPI](https://pypi.org/project/fastcluster/) and [my GitHub
repository](https://github.com/dmuellner/fastcluster/) host the source code
for the Python interface only. The archive with both the R and the Python
interface is available on
[CRAN](https://CRAN.R-project.org/package=fastcluster) and the GitHub repository
[“cran/fastcluster”](https://github.com/cran/fastcluster). Even though I appear
as the author also of this second GitHub repository, this is just an automatic,
read-only mirror of the CRAN archive, so please do not attempt to report bugs or
contact me via this repository.

Installation files for Windows are provided on [PyPI](
https://pypi.org/project/fastcluster/#files) and on [Christoph Gohlke's web
page](http://www.lfd.uci.edu/~gohlke/pythonlibs/#fastcluster).

Christoph Dalitz wrote a pure [C++ interface to fastcluster](
https://lionel.kr.hs-niederrhein.de/~dalitz/data/hclust/).

Reference: Daniel Müllner, *fastcluster: Fast Hierarchical, Agglomerative
Clustering Routines for R and Python*, Journal of Statistical Software, **53**
(2013), no. 9, 1–18, https://doi.org/10.18637/jss.v053.i09.



            

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    "description": "\nThis library provides Python functions for hierarchical clustering. It\ngenerates hierarchical clusters from distance matrices or from vector data.\n\nThis module is intended to replace the functions\n```\n    linkage, single, complete, average, weighted, centroid, median, ward\n```\nin the module [`scipy.cluster.hierarchy`](\nhttps://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html) with the same\nfunctionality but much faster algorithms. Moreover, the function\n`linkage_vector` provides memory-efficient clustering for vector data.\n\nThe interface is very similar to MATLAB's Statistics Toolbox API to make code\neasier to port from MATLAB to Python/NumPy. The core implementation of this\nlibrary is in C++ for efficiency.\n\n**User manual:** [fastcluster.pdf](\nhttps://github.com/dmuellner/fastcluster/raw/master/docs/fastcluster.pdf).\n\nThe \u201cYule\u201d distance function changed in fastcluster version 1.2.0. This is\nfollowing a [change in SciPy 1.6.3](\nhttps://github.com/scipy/scipy/commit/3b22d1da98dc1b5f64bc944c21f398d4ba782bce).\nIt is recommended to use fastcluster version 1.1.x together with SciPy versions\nbefore 1.6.3 and fastcluster 1.2.x with SciPy \u22651.6.3.\n\nThe fastcluster package is considered stable and will undergo few changes\nfrom now on. If some years from now there have not been any updates, this does\nnot necessarily mean that the package is unmaintained but maybe it just was\nnot necessary to correct anything. Of course, please still report potential\nbugs and incompatibilities to daniel@danifold.net. You may also use\n[my GitHub repository](https://github.com/dmuellner/fastcluster/)\nfor bug reports, pull requests etc.\n\nNote that [PyPI](https://pypi.org/project/fastcluster/) and [my GitHub\nrepository](https://github.com/dmuellner/fastcluster/) host the source code\nfor the Python interface only. The archive with both the R and the Python\ninterface is available on\n[CRAN](https://CRAN.R-project.org/package=fastcluster) and the GitHub repository\n[\u201ccran/fastcluster\u201d](https://github.com/cran/fastcluster). Even though I appear\nas the author also of this second GitHub repository, this is just an automatic,\nread-only mirror of the CRAN archive, so please do not attempt to report bugs or\ncontact me via this repository.\n\nInstallation files for Windows are provided on [PyPI](\nhttps://pypi.org/project/fastcluster/#files) and on [Christoph Gohlke's web\npage](http://www.lfd.uci.edu/~gohlke/pythonlibs/#fastcluster).\n\nChristoph Dalitz wrote a pure [C++ interface to fastcluster](\nhttps://lionel.kr.hs-niederrhein.de/~dalitz/data/hclust/).\n\nReference: Daniel M\u00fcllner, *fastcluster: Fast Hierarchical, Agglomerative\nClustering Routines for R and Python*, Journal of Statistical Software, **53**\n(2013), no. 9, 1\u201318, https://doi.org/10.18637/jss.v053.i09.\n\n\n",
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