cclustr: Consensus Clustering Methods for Multiple Imputed Data
Provides tools for performing consensus clustering on multiple
imputed datasets. The package supports a range of clustering algorithms
across imputations, including hierarchical methods (e.g., Ward, single,
complete, average) and partition-based approaches such as k-means,
k-medoids (PAM), fuzzy clustering, model-based clustering ('mclust'),
and methods for mixed or categorical data (k-modes and k-prototypes).
A co-assignment matrix is constructed to quantify agreement between
partitions, and consensus solutions are derived via hierarchical
clustering applied to the resulting dissimilarity matrix. Additional
functions are provided for validation and visualization of clustering
results, facilitating robust analysis in the presence of missing data.
Consensus clustering framework is based on Monti et al. (2003)
<doi:10.1023/A:1023949509487>, rank aggregation methods follow
Pihur et al. (2007) <doi:10.1093/bioinformatics/btm158>, and the
PAC (Proportion of Ambiguous Clustering) metric is based on
Senbabaoglu et al. (2014) <doi:10.1038/srep06207>.
| Version: |
0.1.1 |
| Depends: |
R (≥ 4.0) |
| Imports: |
cluster, e1071, fpc, graphics, stats, viridisLite, klaR, clustMixType, proxy, mclust |
| Suggests: |
knitr, mice, mlbench, rmarkdown, spelling, testthat (≥
3.0.0), utils |
| Published: |
2026-05-18 |
| DOI: |
10.32614/CRAN.package.cclustr (may not be active yet) |
| Author: |
Anhuar Duran Mendoza [aut],
Andres Montenegro Lemus [aut, cre],
Mario Pacheco Lopez [aut] |
| Maintainer: |
Andres Montenegro Lemus <andresfemole at gmail.com> |
| BugReports: |
https://github.com/andrews06ml/cclustr/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/andrews06ml/cclustr |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
NEWS |
| CRAN checks: |
cclustr results |
Documentation:
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