Projection Based Clustering
24 available
Curvilinear Component Analysis (CCA)
Plot Projected Points
GUI for cutting out an Island.
Interactive Projection-Based Clustering (IPBC)
continuity and trustworthiness
Adjacency matrix of the delaunay graph for BestMatches of Points
Dijkstra SSSP
Independent Component Analysis (ICA)
GUI for interactive cluster analysis
Isomap
Smoothed Precision and Recall
Kruskal stress calculation
Multidimensional Scaling (MDS)
Neighbor Retrieval Visualizer (NeRV)
Principal Component Analysis (PCA)
Polar Swarm (Pswarm)
Projection to Bestmatches
Projection Based Clustering
Automatic Projection-based Clustering (PBC) [Thrun/Ultsch, 2020]
Projection Pursuit
Sammons Mapping
Shortest GraphPaths = geodesic distances
T-distributed Stochastic Neighbor Embedding (t-SNE)
Uniform Manifold Approximation and Projection
A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" <DOI:10.1007/s00357-020-09373-2>. Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, 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.