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 |
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