The R Journal: article published in 2019, volume 11:2

PPCI: an R Package for Cluster Identification using Projection Pursuit
David P. Hofmeyr and Nicos G. Pavlidis , The R Journal (2019) 11:2, pages 152-170.

Abstract This paper presents the R package PPCI which implements three recently proposed projec tion pursuit methods for clustering. The methods are unified by the approach of defining an optimal hyperplane to separate clusters, and deriving a projection index whose optimiser is the vector normal to this separating hyperplane. Divisive hierarchical clustering algorithms that can detect clusters defined in different subspaces are readily obtained by recursively bi-partitioning the data through such hyperplanes. Projecting onto the vector normal to the optimal hyperplane enables visualisations of the data that can be used to validate the partition at each level of the cluster hierarchy. Clustering models can also be modified in an interactive manner to improve their solutions. Extensions to problems involving clusters which are not linearly separable, and to the problem of finding maximum hard margin hyperplanes for clustering are also discussed.

Received: 2018-03-02; online 2019-12-27, supplementary material, (5.7 KiB)
CRAN packages: ProjectionBasedClustering, subspace

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2019-046,
  author = {David P. Hofmeyr and Nicos G. Pavlidis},
  title = {{PPCI: an R Package for Cluster Identification using
          Projection Pursuit}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-046},
  url = {https://doi.org/10.32614/RJ-2019-046},
  pages = {152--170},
  volume = {11},
  number = {2}
}