Package: PPCI 0.1.5

David Hofmeyr

PPCI: Projection Pursuit for Cluster Identification

Implements recently developed projection pursuit algorithms for finding optimal linear cluster separators. The clustering algorithms use optimal hyperplane separators based on minimum density, Pavlidis et. al (2016) <https://jmlr.csail.mit.edu/papers/volume17/15-307/15-307.pdf>; minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>; and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.

Authors:David Hofmeyr <dhofmeyr@sun.ac.za> [aut, cre] Nicos Pavlidis <n.pavlidis@lancaster.ac.uk> [aut]

PPCI_0.1.5.tar.gz
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PPCI.pdf |PPCI.html✨
PPCI/json (API)

# Install 'PPCI' in R:
install.packages('PPCI', repos = c('https://davidhofmeyr.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/davidhofmeyr/ppci/issues0 issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • breastcancer - Discrimination of Cancerous and Non-Cancerous Breast Masses
  • dermatology - Eryhemato-Squamous Disease Identification
  • optidigits - Optical Recognition of Handwritten Digits
  • pendigits - Pen-based Recognition of Handwritten Digits
  • phoneme - Speech Recognition through Phoneme Identification
  • yale - Face Recognition

On CRAN:PPCI-0.1.5(2020-03-06)

Conda-Forge:

openblascpp

3.26 score 2 stars 18 scripts 236 downloads 44 exports 7 dependencies

Last updated 5 years agofrom:aa8fa12f6c. Checks:1 OK, 10 WARNING. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 11 2025
R-4.5-win-x86_64WARNINGFeb 11 2025
R-4.5-mac-x86_64WARNINGFeb 11 2025
R-4.5-mac-aarch64WARNINGFeb 11 2025
R-4.5-linux-x86_64WARNINGFeb 11 2025
R-4.4-win-x86_64WARNINGFeb 11 2025
R-4.4-mac-x86_64WARNINGFeb 11 2025
R-4.4-mac-aarch64WARNINGFeb 11 2025
R-4.3-win-x86_64WARNINGFeb 11 2025
R-4.3-mac-x86_64WARNINGFeb 11 2025
R-4.3-mac-aarch64WARNINGFeb 11 2025

Exports:add_subtreecluster_performancedf_mcdf_mddf_md_cppdf_ncutdncut_xf_mcf_mdf_md_cppf_ncuthp_plotis_minimismin_cppmc_bmcdcmcdrmchmcppmd_bmd_b_cppmd_reldepthmddcmddrmdhmdppncut_bncut_xncutdcncutdrncuthncutppnode_plotnorm_vecoptidigits_mean_imagesplot.ppci_cluster_solutionplot.ppci_hyperplane_solutionplot.ppci_projection_solutionppclust.optimsubtree_widthsuccess_ratiotree_plottree_prunetree_split

Dependencies:latticeMatrixrARPACKRcppRcppArmadilloRcppEigenRSpectra

Citation

The following are references to the package. You should also reference the individual methods used, as detailed in the reference section of the help files for each function.

Hofmeyr DP, Pavlidis NG (2019). “PPCI: an R Package for Cluster Identification using Projection Pursuit.” The R Journal. doi:10.32614/RJ-2019-046, https://journal.r-project.org/archive/2019/RJ-2019-046/index.html.

To get Bibtex entries use: x<-citation("PPCI"); toBibtex(x)

Corresponding BibTeX entry: 

  @Article{,
    title = {{PPCI}: an {R} Package for Cluster Identification using
      Projection Pursuit},
    author = {David P. Hofmeyr and Nicos G. Pavlidis},
    journal = {{The R Journal}},
    year = {2019},
    doi = {10.32614/RJ-2019-046},
    url =
      {https://journal.r-project.org/archive/2019/RJ-2019-046/index.html},
  }