Package: genieclust 1.3.0.9001

genieclust: Genie: Fast and Robust Hierarchical Clustering

Genie is a robust hierarchical clustering algorithm (Gagolewski, Bartoszuk, Cena, 2016 <doi:10.1016/j.ins.2016.05.003>). 'genieclust' is its faster, more capable implementation (Gagolewski, 2021 <doi:10.1016/j.softx.2021.100722>). It enables clustering with respect to mutual reachability distances, allowing it to act as an alternative to 'HDBSCAN*' that can identify any number of clusters or their entire hierarchy. When combined with the 'deadwood' package, it can act as an outlier detector. Additional package features include the Gini and Bonferroni inequality indices, external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). The 'Python' version of 'genieclust' is available via 'PyPI'.

Authors:Marek Gagolewski [aut, cre, cph], Maciej Bartoszuk [ctb], Anna Cena [ctb], Peter M. Larsen [ctb]

genieclust_1.3.0.9001.tar.gz
genieclust_1.3.0.9001.zip(r-4.7)genieclust_1.3.0.9001.zip(r-4.6)genieclust_1.3.0.9001.zip(r-4.5)
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manual.pdf |manual.html
card.svg |card.png
genieclust/json (API)
NEWS

# Install 'genieclust' in R:
install.packages('genieclust', repos = c('https://gagolews.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gagolews/genieclust/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cluster-analysisclusteringclustering-algorithmdata-analysisdata-miningdata-sciencegeniehdbscanhierarchical-clusteringhierarchical-clustering-algorithmmachine-learningmachine-learning-algorithmsmlpacknmslibpythonpython3sparsecpp

7.90 score 73 stars 6 packages 14 scripts 577 downloads 26 exports 3 dependencies

Last updated from:fbb934c540. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK115
linux-devel-x86_64OK111
source / vignettesOK206
linux-release-arm64OK112
linux-release-x86_64OK110
macos-release-arm64OK83
macos-release-x86_64OK184
macos-oldrel-arm64OK86
macos-oldrel-x86_64OK211
windows-develOK161
windows-releaseOK127
windows-oldrelOK93
wasm-releaseFAIL92

Exports:adjusted_fm_scoreadjusted_mi_scoreadjusted_rand_scorebonferroni_indexcalinski_harabasz_indexdevergottini_indexdunnowa_indexfm_scoregclustgeneralised_dunn_indexgeniegini_indexmi_scorenegated_ball_hall_indexnegated_davies_bouldin_indexnegated_wcss_indexnormalized_clustering_accuracynormalized_confusion_matrixnormalized_mi_scorenormalized_pivoted_accuracynormalizing_permutationpair_sets_indexrand_scoresilhouette_indexsilhouette_w_indexwcnn_index

Dependencies:deadwoodquitefastmstRcpp

Readme and manuals

Help Manual

Help pageTopics
Internal Cluster Validity Measurescalinski_harabasz_index cluster_validity dunnowa_index generalised_dunn_index negated_ball_hall_index negated_davies_bouldin_index negated_wcss_index silhouette_index silhouette_w_index wcnn_index
External Cluster Validity Measures and Pairwise Partition Similarity Scoresadjusted_fm_score adjusted_mi_score adjusted_rand_score compare_partitions fm_score mi_score normalized_clustering_accuracy normalized_confusion_matrix normalized_mi_score normalized_pivoted_accuracy normalizing_permutation pair_sets_index rand_score
Hierarchical Clustering Algorithm Geniegclust gclust.default gclust.dist gclust.mst genie genie.default genie.dist genie.mst
Inequality Measuresbonferroni_index devergottini_index gini_index inequality