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Implementation of the BRIk, FABRIk and FDEBRIk algorithms to initialise k-means. These methods are intended for the clustering of multivariate and functional data, respectively. They make use of the Modified Band Depth and bootstrap to identify appropriate initial seeds for k-means, which are proven to be better options than many techniques in the literature. Torrente and Romo (2021) <doi:10.1007/s00357-020-09372-3> It makes use of the functions kma and kma.similarity, from the archived package fdakma, by Alice Parodi et al.
| Version: | 1.0 |
| Depends: | R (≥ 3.1.0), boot, cluster, depthTools, splines, splines2, stats |
| Imports: | methods |
| Published: | 2022-07-21 |
| Author: | Javier Albert Smet and Aurora Torrente. Alice Parodi, Mirco Patriarca, Laura Sangalli, Piercesare Secchi, Simone Vantini and Valeria Vitelli, as contributors. |
| Maintainer: | Aurora Torrente <etorrent at est-econ.uc3m.es> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | briKmeans results |
| Reference manual: | briKmeans.pdf |
| Package source: | briKmeans_1.0.tar.gz |
| Windows binaries: | r-devel: briKmeans_1.0.zip, r-release: briKmeans_1.0.zip, r-oldrel: briKmeans_1.0.zip |
| macOS binaries: | r-release (arm64): briKmeans_1.0.tgz, r-oldrel (arm64): briKmeans_1.0.tgz, r-release (x86_64): briKmeans_1.0.tgz, r-oldrel (x86_64): briKmeans_1.0.tgz |
| Old sources: | briKmeans archive |
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