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Fast hierarchical clustering routines for R and Python.

Project description

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.

Installation files for Windows are provided by Christoph Gohlke on his web page.

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 muellner@math.stanford.edu.

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/.

Project details


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1.1.24

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1.1.23

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1.1.22

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1.1.21

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1.1.20

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1.1.18

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1.1.17

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1.1.13

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1.1.12

This version
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1.1.11

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1.1.9

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1.1.8

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1.1.7

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1.1.6

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