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genieclust 1.1.6

genieclust Package for R and Python
Genie: Fast and Robust Hierarchical Clustering with Noise Point Detection

Genie finds meaningful clusters quickly – even on large data sets.
A comprehensive tutorial, benchmarks, and a reference manual is available
at https://genieclust.gagolewski.com/.

When using genieclust in research publications, please
cite (Gagolewski, 2021) and (Gagolewski, Bartoszuk, Cena, 2016)
as specified below. Thank you.
About
A faster and more powerful version of Genie – a robust and outlier
resistant clustering algorithm (see Gagolewski, Bartoszuk, Cena, 2016),
originally included in the R package
genie.
The idea behind Genie is beautifully simple. First, make each individual
point the only member of its own cluster. Then, keep merging pairs
of the closest clusters, one after another. However, to prevent
the formation of clusters of highly imbalanced sizes a point group of
the smallest size will sometimes be matched with its nearest neighbour.
Genie's appealing simplicity goes hand in hand with its usability;
it often outperforms other clustering approaches
such as K-means, BIRCH, or average, Ward, and complete linkage
on benchmark data.
Of course, there is no, nor will there ever be, a single best
universal clustering approach for every kind of problem, but Genie
is definitely worth a try!
Thanks to its being based on minimal spanning trees of the pairwise distance
graphs, Genie is also very fast – determining the whole cluster hierarchy
for datasets of millions of points can be completed within minutes. Therefore,
it is nicely suited for solving extreme clustering tasks (large datasets
with any number of clusters to detect) for data (also sparse) that fit into
memory. Thanks to the use of nmslib
(if available), sparse or string inputs are also supported.
It also allows clustering with respect to mutual reachability distances
so that it can act as a noise point detector or a
robustified version of HDBSCAN* (see Campello et al., 2013)
that is able to detect a predefined
number of clusters and hence it doesn't dependent on the DBSCAN's somewhat
difficult-to-set eps parameter.
The package also features an implementation of economic inequality indices
(the Gini, Bonferroni index), external cluster validity measures
(e.g., the normalised clustering accuracy and partition similarity scores
such as the adjusted Rand, Fowlkes-Mallows, adjusted mutual information,
and the pair sets index), and internal cluster validity indices
(e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette,
and generalised Dunn indices).
Author and Contributors
Author and Maintainer: Marek Gagolewski
Contributors:
Maciej Bartoszuk,
Anna Cena (R packages
genie
and CVI),
Peter M. Larsen
(rectangular_lsap).
Examples, Tutorials, and Documentation
R's interface is compatible with stats::hclust(), but there is more.
X <- ... # some data
h <- gclust(X)
plot(h) # plot cluster dendrogram
cutree(h, k=2)
# or genie(X, k=2)

To learn more about R, check out Marek's open-access (free!) textbook
Deep R Programming.
The Python language version of genieclust has a familiar
scikit-learn-like look-and-feel:
import genieclust
X = ... # some data
g = genieclust.Genie(n_clusters=2)
labels = g.fit_predict(X)

Tutorials and the package documentation are available
here.
To learn more about Python, check out Marek's recent open-access (free!) textbook
Minimalist Data Wrangling in Python.
How to Install
Python Version
To install via pip (see PyPI):
pip3 install genieclust

The package requires Python 3.7+ together with cython as well as
numpy, scipy, matplotlib, and scikit-learn.
Optional dependencies: nmslib and mlpack.
R Version
To install the most recent release, call:
install.packages("genieclust")

See the package entry on
CRAN.
Other
The core functionality is implemented in the form of a header-only
C++ library. It can thus be easily adapted for use in
other environments.
Any contributions are welcome (e.g., Julia, Matlab, ...).
License
Copyright (C) 2018–2024 Marek Gagolewski https://www.gagolewski.com/
This program is free software: you can redistribute it and/or modify it
under the terms of the GNU Affero General Public License Version 3, 19
November 2007, published by the Free Software Foundation.
This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero
General Public License Version 3 for more details. You should have
received a copy of the License along with this program. If not, see
(https://www.gnu.org/licenses/).

The file src/c_scipy_rectangular_lsap.h is adapted from the
scipy project (https://scipy.org/scipylib), source:
/scipy/optimize/rectangular_lsap/rectangular_lsap.cpp.
Author: Peter M. Larsen. Distributed under the BSD-3-Clause license.
The implementation of internal cluster validity measures
were adapted from our previous project (Gagolewski, Bartoszuk, Cena, 2021);
see optim_cvi.
Originally distributed under the GNU Affero General Public License Version 3.
References
Gagolewski M., genieclust: Fast and robust hierarchical clustering,
SoftwareX 15, 2021, 100722.
DOI: 10.1016/j.softx.2021.100722.
https://genieclust.gagolewski.com/.
Gagolewski M., Bartoszuk M., Cena A., Genie: A new, fast, and
outlier-resistant hierarchical clustering algorithm, Information
Sciences 363, 2016, 8–23.
DOI: 10.1016/j.ins.2016.05.003.
Gagolewski M., Bartoszuk M., Cena A., Are cluster validity measures (in)valid?,
Information Sciences 581, 2021, 620–636.
DOI: 10.1016/j.ins.2021.10.004.
Gagolewski M., Cena A., Bartoszuk M., Brzozowski L.,
Clustering with minimum spanning trees: How good can it be?,
Journal of Classification, 2024, in press,
DOI: 10.1007/s00357-024-09483-1.
Gagolewski M., Normalised clustering accuracy: An asymmetric external
cluster validity measure, Journal of Classification, 2024, in press,
DOI: 10.1007/s00357-024-09482-2.
Gagolewski M., A framework for benchmarking clustering algorithms,
SoftwareX 20, 2022, 101270.
DOI: 10.1016/j.softx.2022.101270.
https://clustering-benchmarks.gagolewski.com/.
Campello R.J.G.B., Moulavi D., Sander J.,
Density-based clustering based on hierarchical density estimates,
Lecture Notes in Computer Science 7819, 2013, 160–172.
DOI: 10.1007/978-3-642-37456-2_14.
Mueller A., Nowozin S., Lampert C.H., Information theoretic clustering
using minimum spanning trees, DAGM-OAGM, 2012.
Rezaei M., Fränti P., Set matching measures for external cluster validity,
IEEE Transactions on Knowledge and Data Engineering 28(8), 2016,
2173–2186 DOI: 10.1109/TKDE.2016.2551240.
See the package's homepage for more
references.