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  "Title": "Projection Based Clustering",
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  "Authors@R": "c(person(\"Michael\", \"Thrun\", email= \"m.thrun@gmx.net\",role=c(\"aut\",\"cre\",\"cph\")),person(\"Florian\", \"Lerch\",role=\"aut\"),person(\"Felix\", \"Pape\",role=\"aut\"),person(\"Tim\", \"Schreier\",role=\"aut\"),person(\"Luis\", \"Winckelmann\",role=\"aut\"),person(\"Kristian\", \"Nybo\",role=\"cph\"),person(\"Jarkko\", \"Venna\",role=\"cph\"))",
  "Date": "2022-05-31",
  "Description": "A clustering approach applicable to every projection\nmethod is proposed here. The two-dimensional scatter plot of\nany projection method can construct a topographic map which\ndisplays unapparent data structures by using distance and\ndensity information of the data. The generalized U*-matrix\nrenders this visualization in the form of a topographic map,\nwhich can be used to automatically define the clusters of\nhigh-dimensional data. The whole system is based on Thrun and\nUltsch, \"Using Projection based Clustering to Find Distance and\nDensity based Clusters in High-Dimensional Data\"\n<DOI:10.1007/s00357-020-09373-2>. Selecting the correct\nprojection method will result in a visualization in which\nmountains surround each cluster. The number of clusters can be\ndetermined by counting valleys on the topographic map. Most\nprojection methods are wrappers for already available methods\nin R. By contrast, the neighbor retrieval visualizer (NeRV) is\nbased on C++ source code of the 'dredviz' software package, and\nthe Curvilinear Component Analysis (CCA) is translated from\n'MATLAB' ('SOM Toolbox' 2.0) to R.",
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  "URL": "https://www.deepbionics.org",
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  "Date/Publication": "2022-05-31 15:26:55 UTC",
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