Fuzzy Unsupervised and Semi-Supervised Clustering
Overview
Methods for distance-based fuzzy unsupervised and semi-supervised clustering, including fuzzy and possibilistic models based on alternating optimization (AO) algorithm. The package introduces a vectorized estimation framework for prototype-based fuzzy clustering algorithms, enabling modular algorithm design and extensibility. It also supports storage and retrieval of intermediate AO optimization results for downstream analysis and processing. For more details see Kmita et al. (2024) doi:10.1109/TFUZZ.2024.3370768.
- Initial CRAN submission.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK6 OK2026-06-03
Documentation
- Help pages
- 17
- Examples that run
- 100%6 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 24%
Downloads
Dependencies
Installing fussclust adds 3 packages (3 with compiled code, 2.8 MB of source tarballs). Heaviest: Rcpp 1.8 MB, RcppArmadillo 1.0 MB, rdist 14 KB.
Code & Tests
Datasets
People & History
- 0.1.0Latest2026-06-02 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-06-02
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.1.0
- Bundled data
- 4.2 KB / 2 files
- Download size
- 27 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("fussclust")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.