Implements the methodology proposed by Anderlucci, Fortunato and Montanari (2019) <arXiv:1909.10832> for high-dimensional unsupervised classification. The random projection ensemble clustering algorithm applies a Gaussian Mixture Model to different random projections of the high-dimensional data and selects a subset of solutions accordingly to the Bayesian Information Criterion, computed here as discussed in Raftery and Dean (2006) <doi:10.1198/016214506000000113>. The clustering results obtained on the selected projections are then aggregated via consensus to derive the final partition.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.6.0), clusteval |
| Imports: | mclust, clue |
| Published: | 2019-11-06 |
| Author: | L. Anderlucci [aut], F. Fortunato [aut, cre], A. Montanari [ctb] |
| Maintainer: | Francesca Fortunato <francesca.fortunato3 at unibo.it> |
| License: | GPL-3 |
| URL: | https://arxiv.org/abs/1909.10832 |
| NeedsCompilation: | no |
| CRAN checks: | RPEClust results |
| Reference manual: | RPEClust.pdf |
| Package source: | RPEClust_0.1.0.tar.gz |
| Windows binaries: | r-devel: RPEClust_0.1.0.zip, r-release: RPEClust_0.1.0.zip, r-oldrel: RPEClust_0.1.0.zip |
| macOS binaries: | r-release: RPEClust_0.1.0.tgz, r-oldrel: RPEClust_0.1.0.tgz |
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