Projection Pursuit for Cluster Identification
Evaluate External Valifity os a Binary Partition
Visualise a Hierarchical Clustering Model
Prune a Hierarchical Clustering Model
Split a Leaf in a Hierarchical Clustering Model
Add Nodes To a Plot of a Hierarchical Clustering Model
External Cluster Validity Metrics
Gradient of the Variance Ratio Clusterability Across a Hyperplane
Gradient of the Integrated Density on a Hyperplane
Gradient of the Normalised Cut Across a Hyperplane
Gradient of the Penalised Density at a Point
Variance Ratio Clusterability Across a Hyperplane
Integrated Density on a Hyperplane
Normalised Cut Across a Hyperplane
Visualise a Hyperplane Separator for Clustering
Check if the Current Solution is a Valid Minimum Density Hyperplane
Location of Optimal Variance Ratio Hyperplane
Divisive Clustering Using Maximum Clusterability
Maximum Clusterability Dimension Reduction
Maximum Clusteriability Hyperplane
Maximum Clusterability Projection Pursuit
Location of Minimum Density Hyperplane
Relative Depth of a Hyperplane
Divisive Clustering Using Minimum Density Hyperplanes
Minimum Density Dimension Reduction
Minimum Density Hyperplane
Minimum Density Projection Pursuit
Location of Minimum Normalised Cut Hyperplane
Divisive Clustering Using Minimum Normalised Cut Hyperplanes
Minimum Normalised Cut Dimension Reduction
Minimum Normalised Cut Hyperplane
Minimum Normalised Cut Projection Pursuit
Visualise a Node in a Hierarchical Clustering Model
Euclidean Norm of a Vector
Visualise Cluster Means from optidigits data set
Pen-based Recognition of Handwritten Digits
Visualise a Hierarchical Clustering Model, or a Node Within a Hierarch...
Visualise a Hyperplane Separator for Clustering
Visualise a Data Set Projected from Projection Pursuit
tools:::Rd_package_title("PPCI")
Optimisation Call for Projection Pursuit Algorithms
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>.