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>.

Version: 0.1.0
Depends: R (≥ 2.10.0)
Imports: rARPACK
Published: 2017-11-22
Author: David Hofmeyr [aut, cre] Nicos Pavlidis [aut]
Maintainer: David Hofmeyr <dhofmeyr at sun.ac.za>
License: GPL-3
NeedsCompilation: no
CRAN checks: PPCI results

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Reference manual: PPCI.pdf
Package source: PPCI_0.1.0.tar.gz
Windows binaries: r-devel: PPCI_0.1.0.zip, r-release: PPCI_0.1.0.zip, r-oldrel: PPCI_0.1.0.zip
OS X El Capitan binaries: r-release: PPCI_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: PPCI_0.1.0.tgz

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