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A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; <doi:10.18637/jss.v091.i10>). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <doi:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <doi:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <doi:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <doi:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; <doi:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; <doi:10.1002/wics.1456>), which combine PCA for mixed-type data with K-means.
| Version: | 1.4.0 |
| Depends: | ggplot2, grid |
| Imports: | rARPACK, tibble, corpcor, GGally, fpc, cluster, dplyr, plyr, ggrepel, ca, stats |
| Published: | 2022-07-16 |
| DOI: | 10.32614/CRAN.package.clustrd |
| Author: | Angelos Markos [aut, cre], Alfonso Iodice D'Enza [aut], Michel van de Velden [aut] |
| Maintainer: | Angelos Markos <amarkos at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Citation: | clustrd citation info |
| CRAN checks: | clustrd results |
| Reference manual: | clustrd.pdf |
| Package source: | clustrd_1.4.0.tar.gz |
| Windows binaries: | r-devel: clustrd_1.4.0.zip, r-release: clustrd_1.4.0.zip, r-oldrel: clustrd_1.4.0.zip |
| macOS binaries: | r-release (arm64): clustrd_1.4.0.tgz, r-oldrel (arm64): clustrd_1.4.0.tgz, r-release (x86_64): clustrd_1.4.0.tgz, r-oldrel (x86_64): clustrd_1.4.0.tgz |
| Old sources: | clustrd archive |
| Reverse suggests: | clusterMI, FCPS |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.