PPCI : an R package for cluster identification using projection pursuit
Date
2019-12
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Projection pursuit (Statistics) -- Methods
Abstract
This paper presents the R package PPCI which implements three recently proposed projection 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.
Description
CITATION: Hofmeyr, D. P. & Pavlidis, N. G. 2019. PPCI: an R Package for Cluster Identification using Projection Pursuit. The R Journal, 11(2):152-170. doi:10.32614/RJ-2019-046
The original publication is available at https://journal.r-project.org/archive/2019/RJ-2019-046/index.html
The original publication is available at https://journal.r-project.org/archive/2019/RJ-2019-046/index.html
Keywords
R (Computer program language) -- Projection Pursuit for Cluster Identification (PPCI)
Citation
Hofmeyr, D. P. & Pavlidis, N. G. 2019. PPCI: an R Package for Cluster Identification using Projection Pursuit. The R Journal, 11(2):152-170. doi:10.32614/RJ-2019-046