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) <http://jmlr.org/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>.

Version: 0.1.5
Depends: R (≥ 2.10.0), rARPACK
Published: 2020-03-06
Author: David Hofmeyr [aut, cre] Nicos Pavlidis [aut]
Maintainer: David Hofmeyr <dhofmeyr at sun.ac.za>
License: GPL-3
NeedsCompilation: no
Citation: PPCI citation info
CRAN checks: PPCI results

Documentation:

Reference manual: PPCI.pdf

Downloads:

Package source: PPCI_0.1.5.tar.gz
Windows binaries: r-devel: PPCI_0.1.5.zip, r-release: PPCI_0.1.5.zip, r-oldrel: PPCI_0.1.5.zip
macOS binaries: r-release (arm64): PPCI_0.1.5.tgz, r-release (x86_64): PPCI_0.1.5.tgz, r-oldrel: PPCI_0.1.5.tgz
Old sources: PPCI archive

Reverse dependencies:

Reverse suggests: FCPS

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