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ProjectionBasedClustering (version 1.0.1)

Projection Based Clustering

Description

A clustering approach for every projection method based on the generalized U*-matrix visualization of a topographic map is made available here. The number of clusters and the cluster structure can be estimated by counting the valleys in a topographic map. If the number of clusters and the clustering method are chosen correctly, then the clusters will be well separated by mountains in the visualization. Most projection methods are wrappers for already available methods in R. However, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R.

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Version

Install

install.packages('ProjectionBasedClustering')

Monthly Downloads

753

Version

1.0.1

License

GPL-3

Maintainer

Michael Thrun

Last Published

July 24th, 2017

Functions in ProjectionBasedClustering (1.0.1)

MDS

Classical multidimensional scaling (MDS)
NeRV

NeRV projection
Isomap

Isomap projection method
KruskalStress

Kruskal stress calculation
Delaunay4Points

Adjacency matrix of the delaunay graph for BestMatches of Points
DijkstraSSSP

Dijkstra SSSP
ShepardDiagram

Draw a Shepard diagram
ShortestGraphPathsC

Shortest GraphPaths = geodesic distances
Hepta

Hepta from FCPS
ICA

Independent Component Analysis)
ProjectionPursuit

Projection Pursuit
SammonsMapping

Sammons Mapping
CCA

Curvilinear Component Analysis
DefaultColorSequence

Default color sequence for plots
klrank

Computes rank-based smoothed precision/recall
ProjectionBasedClustering-package

Projection Based Clustering
ProjectionBasedClustering

automated Clustering approach of the Databonic swarm with abstact U distances
PCA

Principal component analysis
PlotProjectedPoints

Plot Projected Points
tSNE

T-distributed Stochastic Neighbor Embedding