PPCI v0.1.5


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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>.

Functions in PPCI

Name Description
dermatology Eryhemato-Squamous Disease Identification
breastcancer Discrimination of Cancerous and Non-Cancerous Breast Masses
dkde Gradient of the Penalised Density at a Point
add_subtree Add Nodes To a Plot of a Hierarchical Clustering Model
mch Maximum Clusteriability Hyperplane
mc_b Location of Optimal Variance Ratio Hyperplane
mcdr Maximum Clusterability Dimension Reduction
PPCI-package PPCI
hp_plot Visualise a Hyperplane Separator for Clustering
cluster_performance External Cluster Validity Metrics
mcpp Maximum Clusterability Projection Pursuit
ncut_b Location of Minimum Normalised Cut Hyperplane
f_md Integrated Density on a Hyperplane
md_b Location of Minimum Density Hyperplane
mdpp Minimum Density Projection Pursuit
f_ncut Normalised Cut Across a Hyperplane
ncutdc Divisive Clustering Using Minimum Normalised Cut Hyperplanes
ncutdr Minimum Normalised Cut Dimension Reduction
mcdc Divisive Clustering Using Maximum Clusterability
pendigits Pen-based Recognition of Handwritten Digits
mdh Minimum Density Hyperplane
plot.ppci_cluster_solution Visualise a Hierarchical Clustering Model, or a Node Within a Hierarchical Clustering Model
yale Face Recognition
md_reldepth Relative Depth of a Hyperplane
is_minim Check if the Current Solution is a Valid Minimum Density Hyperplane
mddr Minimum Density Dimension Reduction
optidigits Optical Recognition of Handwritten Digits
optidigits_mean_images Visualise Cluster Means from optidigits data set
ppclust.optim Optimisation Call for Projection Pursuit Algorithms
ncuth Minimum Normalised Cut Hyperplane
subtree_width Determine the Largest Number of Nodes at Any Depth in a Clustering Hierarchy
tree_prune Prune a Hierarchical Clustering Model
plot.ppci_hyperplane_solution Visualise a Hyperplane Separator for Clustering
mddc Divisive Clustering Using Minimum Density Hyperplanes
tree_split Split a Leaf in a Hierarchical Clustering Model
ncutpp Minimum Normalised Cut Projection Pursuit
node_plot Visualise a Node in a Hierarchical Clustering Model
norm_vec Euclidean Norm of a Vector
success_ratio Evaluate External Valifity os a Binary Partition
plot.ppci_projection_solution Visualise a Data Set Projected from Projection Pursuit
tree_plot Visualise a Hierarchical Clustering Model
df_ncut Gradient of the Normalised Cut Across a Hyperplane
f_mc Variance Ratio Clusterability Across a Hyperplane
df_md Gradient of the Integrated Density on a Hyperplane
df_mc Gradient of the Variance Ratio Clusterability Across a Hyperplane
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Type Package
License GPL-3
Encoding UTF-8
LazyData yes
RoxygenNote 7.0.2
NeedsCompilation no
Packaged 2020-03-06 09:20:06 UTC; dhofmeyr
Repository CRAN
Date/Publication 2020-03-06 10:10:01 UTC

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