https://github.com/gagolews/genie

Genie: A Fast and Robust Hierarchical Clustering Algorithm (this R package has now been superseded by genieclust)
https://github.com/gagolews/genie

cluster cluster-analysis clustering data-analysis data-mining data-science datascience genie hierarchical-clustering-algorithm machine-learning machine-learning-algorithms outliers r

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Genie: A Fast and Robust Hierarchical Clustering Algorithm (this R package has now been superseded by genieclust)

• Host: GitHub
• URL: https://github.com/gagolews/genie
• Owner: gagolews
• Created: 2016-02-15T11:04:19.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2022-08-16T01:38:12.000Z (over 2 years ago)
• Last Synced: 2024-10-26T23:12:22.258Z (about 2 months ago)
• Topics: cluster, cluster-analysis, clustering, data-analysis, data-mining, data-science, datascience, genie, hierarchical-clustering-algorithm, machine-learning, machine-learning-algorithms, outliers, r
• Language: C++
• Homepage: http://genieclust.gagolewski.com/
• Size: 409 KB
• Stars: 22
• Watchers: 5
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Genie (R Package)

> This project has been superseded by

[genieclust](https://genieclust.gagolewski.com)

(see also: [GitHub](https://github.com/gagolews/genieclust/)),

which features a faster and more feature-rich implementation

of Genie (available for both R and Python).

## A Fast and Robust Hierarchical Clustering Algorithm

The time needed to apply a hierarchical clustering algorithm

is most often dominated by the number of computations of a pairwise

dissimilarity measure. Such a constraint, for larger data sets,

puts the use of all the classical linkage criteria at a disadvantage,

with the exception of the single linkage one. However, it is known that the single

linkage clustering algorithm is very sensitive to outliers, produces highly

skewed dendrograms and therefore usually does not reflect the true

underlying structure of analysed data - unless the clusters are well-separated.

To overcome its limitations, we proposed a hierarchical clustering linkage

criterion called *Genie*. Namely, our algorithm links two clusters in such

a way that the Gini measure of inequity of the cluster sizes

does not exceed a given threshold.

This method most often outperforms the Ward or average linkage in terms of

the clustering quality on benchmark data. At the same time,

Genie retains the high speed of the single linkage approach,

therefore it is also suitable for analysing larger data sets.

The algorithm is easily parallelisable and thus may be run

on multiple threads to speed up its execution further on.

Its memory overhead is small: there is no need to precompute the complete

distance matrix to perform the computations in order to obtain a desired

clustering.

A detailed description of the algorithm can be found in:

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](https://dx.doi.org/10.1016/j.ins.2016.05.003).

See also:

Gagolewski M., genieclust: Fast and robust hierarchical clustering,

*SoftwareX* **15**, 2021, 100722.

[doi:10.1016/j.softx.2021.100722](https://dx.doi.org/10.1016/j.softx.2021.100722).

**Authors**: [Marek Gagolewski](https://www.gagolewski.com/),

[Maciej Bartoszuk](http://bartoszuk.rexamine.com), and

[Anna Cena](http://cena.rexamine.com)

**CRAN entry**: 

**genieclust**: ,

,