PPCI v0.1.5
0
Monthly downloads
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 | |
| No Results! | ||
Last month downloads
Details
| 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 |
| depends | R (>= 2.10.0) , rARPACK |
| Contributors | David Hofmeyr |
Include our badge in your README
[](http://www.rdocumentation.org/packages/PPCI)