ProjectionBasedClustering
Projection Based Clustering
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.
- Version1.2.2
- R versionunknown
- LicenseGPL-3
- Needs compilation?Yes
- ProjectionBasedClustering citation info
- Last release06/14/2024
Documentation
Team
Michael Thrun
Florian Lerch
Show author detailsRolesAuthorQuirin Stier
Show author detailsRolesContributor, ReviewerFelix Pape
Show author detailsRolesAuthorLuis Winckelmann
Show author detailsRolesAuthorTim Schreier
Show author detailsRolesAuthorBrinkmann Luca
Show author detailsRolesContributorKristian Nybo
Show author detailsRolesCopyright holderJarkko Venna
Show author detailsRolesCopyright holdervan der Maaten Laurens
Show author detailsRolesCopyright holder
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