The Genie++ Hierarchical Clustering Algorithm with Noise Points
Detection
c(
person("Marek", "Gagolewski",
role = c("aut", "cre", "cph"),
email = "marek@gagolewski.com",
comment = c(ORCID = "0000-0003-0637-6028")),
person("Maciej", "Bartoszuk", role = c("ctb")),
person("Anna", "Cena", role = c("ctb")),
person("Peter M.", "Larsen", role = c("ctb"))
)
A retake on the Genie algorithm - a robust
hierarchical clustering method
(Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>).
Now faster and more memory efficient; determining the whole hierarchy
for datasets of 10M points in low dimensional Euclidean spaces or
100K points in high-dimensional ones takes only 1-2 minutes.
Allows clustering with respect to mutual reachability distances
so that it can act as a noise point detector or a robustified version of
'HDBSCAN*' (that is able to detect a predefined number of
clusters and hence it does not dependent on the somewhat
fragile 'eps' parameter).
The package also features an implementation of economic inequity indices
(the Gini, Bonferroni index) and external cluster validity measures
(partition similarity scores; e.g., the adjusted Rand, Fowlkes-Mallows,
adjusted mutual information, pair sets index).
See also the 'Python' version of 'genieclust' available on 'PyPI', which
supports sparse data, more metrics, and even larger datasets.
Marek Gagolewski [aut, cre, cph]
(<https://orcid.org/0000-0003-0637-6028>),
Maciej Bartoszuk [ctb],
Anna Cena [ctb],
Peter M. Larsen [ctb]

A retake on the Genie algorithm
(Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>) - a robust
hierarchical clustering method
(Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>).
Now faster and more memory efficient; determining the whole hierarchy
for datasets of 10M points in low dimensional Euclidean spaces or
100K points in high-dimensional ones takes only 1-2 minutes.
Allows clustering with respect to mutual reachability distances
so that it can act as a noise point detector or a robustified version of
'HDBSCAN*' (that is able to detect a predefined number of
clusters and hence it does not dependent on the somewhat
fragile 'eps' parameter).
The package also features an implementation of economic inequity indices
(the Gini, Bonferroni index) and external cluster validity measures
(partition similarity scores; e.g., the adjusted Rand, Fowlkes-Mallows,
adjusted mutual information, pair sets index).
See also the 'Python' version of 'genieclust' available on 'PyPI', which
supports sparse data, more metrics, and even larger datasets.