Fast, Robust, and Outlier Resistant Hierarchical Clustering

Includes the reference implementation of Genie - a hierarchical clustering algorithm that links two point groups in such a way that an inequity measure (namely, the Gini index) of the cluster sizes does not significantly increase above a given threshold. This method most often outperforms many other data segmentation approaches in terms of clustering quality as tested on a wide range of benchmark datasets. At the same time, Genie retains the high speed of the single linkage approach, therefore it is also suitable for analysing larger data sets. For more details see (Gagolewski et al. 2016 ). For an even faster and more feature-rich implementation, including, amongst others, noise point detection, see the 'genieclust' package.


               genie package NEWS and CHANGELOG


1.0.4 (2017-04-27)

  • Invalid DOI corrected.

1.0.3 (2017-04-27)

  • [BUILD TIME] Registering native routines and disabling symbol search.

1.0.1 (2016-05-25)

  • Updated documentation and package metadata.

The algorithm's description can now be found in:

Gagolewski M., Bartoszuk M., Cena A., Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm, Information Sciences 363, 2016, pp. 8-23, doi:10.1016/j.ins.2016.05.003

See also:

Gagolewski M., Cena A., Bartoszuk M., Hierarchical clustering via penalty-based aggregation and the Genie approach, In: Torra V. et al. (Eds.), Modeling Decisions for Artificial Intelligence (Lecture Notes in Artificial Intelligence 9880), Springer, 2016, pp. 191-202, doi:10.1007/978-3-319-45656-0_16.

1.0.0 (2016-03-07)

  • Initial release.

Reference manual

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1.0.5 by Marek Gagolewski, 3 months ago

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Browse source code at

Authors: Marek Gagolewski [aut, cre, cph] , Maciej Bartoszuk [aut] , Anna Cena [aut]

Documentation:   PDF Manual  

Task views: Robust Statistical Methods

GPL (>= 3) license

Imports Rcpp

Depends on stats, genieclust

Suggests datasets, testthat, stringi

Linking to Rcpp

System requirements: OpenMP, C++11

Suggested by FCPS.

See at CRAN