Fast hierarchical clustering routines for R and Python.
This library provides Python functions for hierarchical clustering. It generates hierarchical clusters from distance matrices or from vector data.
Part of this module is intended to replace the functions ::
linkage, single, complete, average, weighted, centroid, median, ward
in the module scipy.cluster.hierarchy 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>_.
Installation files for Windows are provided on PyPI
<https://pypi.python.org/pypi/fastcluster>_ and on Christoph Gohlke's web
page <http://www.lfd.uci.edu/~gohlke/pythonlibs/#fastcluster>_.
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 [email protected]. You may also use
my GitHub repository <https://github.com/dmuellner/fastcluster/>_
for bug reports, pull requests etc.
Note that PyPI and my GitHub repository 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.
Reference: Daniel Müllner, fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python, Journal of Statistical Software, 53 (2013), no. 9, 1–18, http://www.jstatsoft.org/v53/i09/.