Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python | Müllner | Journal of Statistical Software
Authors: Daniel Müllner
Title: fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python
Abstract: The fastcluster package is a C++ library for hierarchical, agglomerative clustering. It provides a fast implementation of the most efficient, current algorithms when the input is a dissimilarity index. Moreover, it features memory-saving routines for hierarchical clustering of vector data. It improves both asymptotic time complexity (in most cases) and practical performance (in all cases) compared to the existing implementations in standard software: several R packages, MATLAB, Mathematica, Python with SciPy.

Page views:: 17871. Submitted: 2011-10-01. Published: 2013-05-29.
Paper: fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python     Download PDF (Downloads: 19525)
Supplements:
fastcluster_1.1.11.tar.gz: R source package including Python sources Download (Downloads: 805; 165KB)
v53i09.py.zip: v53i09.py: Python example code from the paper Download (Downloads: 855; 843B)
v53i09-benchmarks.zip: Replication files for benchmark results Download (Downloads: 592; 25KB)
iris.txt: Example data in ASCII format Download (Downloads: 834; 14KB)
DOI: 10.18637/jss.v053.i09

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.