PPCI0.1.5 package

Projection Pursuit for Cluster Identification

success_ratio

Evaluate External Valifity os a Binary Partition

tree_plot

Visualise a Hierarchical Clustering Model

tree_prune

Prune a Hierarchical Clustering Model

tree_split

Split a Leaf in a Hierarchical Clustering Model

add_subtree

Add Nodes To a Plot of a Hierarchical Clustering Model

cluster_performance

External Cluster Validity Metrics

df_mc

Gradient of the Variance Ratio Clusterability Across a Hyperplane

df_md

Gradient of the Integrated Density on a Hyperplane

df_ncut

Gradient of the Normalised Cut Across a Hyperplane

dkde

Gradient of the Penalised Density at a Point

f_mc

Variance Ratio Clusterability Across a Hyperplane

f_md

Integrated Density on a Hyperplane

f_ncut

Normalised Cut Across a Hyperplane

hp_plot

Visualise a Hyperplane Separator for Clustering

is_minim

Check if the Current Solution is a Valid Minimum Density Hyperplane

mc_b

Location of Optimal Variance Ratio Hyperplane

mcdc

Divisive Clustering Using Maximum Clusterability

mcdr

Maximum Clusterability Dimension Reduction

mch

Maximum Clusteriability Hyperplane

mcpp

Maximum Clusterability Projection Pursuit

md_b

Location of Minimum Density Hyperplane

md_reldepth

Relative Depth of a Hyperplane

mddc

Divisive Clustering Using Minimum Density Hyperplanes

mddr

Minimum Density Dimension Reduction

mdh

Minimum Density Hyperplane

mdpp

Minimum Density Projection Pursuit

ncut_b

Location of Minimum Normalised Cut Hyperplane

ncutdc

Divisive Clustering Using Minimum Normalised Cut Hyperplanes

ncutdr

Minimum Normalised Cut Dimension Reduction

ncuth

Minimum Normalised Cut Hyperplane

ncutpp

Minimum Normalised Cut Projection Pursuit

node_plot

Visualise a Node in a Hierarchical Clustering Model

norm_vec

Euclidean Norm of a Vector

optidigits_mean_images

Visualise Cluster Means from optidigits data set

pendigits.rd

Pen-based Recognition of Handwritten Digits

plot.ppci_cluster_solution

Visualise a Hierarchical Clustering Model, or a Node Within a Hierarch...

plot.ppci_hyperplane_solution

Visualise a Hyperplane Separator for Clustering

plot.ppci_projection_solution

Visualise a Data Set Projected from Projection Pursuit

PPCI-package

tools:::Rd_package_title("PPCI")

ppclust.optim

Optimisation Call for Projection Pursuit Algorithms

subtree_width

Determine the Largest Number of Nodes at Any Depth in a Clustering Hie...

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>.

  • Maintainer: David Hofmeyr
  • License: GPL-3
  • Last published: 2020-03-06