{
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  "Type": "Package",
  "Title": "K Quantiles Medoids (KQM) Clustering",
  "Version": "1.1.1",
  "Date": "2025-10-13",
  "Authors@R": "c(person(\"Yarong\", \"Yang\", role=c(\"aut\",\"cre\"),email=\"Yi.YA_yaya@hotmail.com\"),\nperson(\"Nader\", \"Ebrahimi\", role=\"ctb\", email=\"nebrahimi@niu.edu\"),\nperson(\"Yoram\", \"Rubin\", role=\"ctb\", email=\"rubin@ce.berkeley.edu\"),\nperson(\"Jacob\", \"Zhang\", role=\"ctb\", email=\"zjz999@gmail.com\"))",
  "Description": "K Quantiles Medoids (KQM) clustering applies quantiles to\ndivide data of each dimension into K mean intervals. Combining\nquantiles of all the dimensions of the data and fully permuting\nquantiles on each dimension is the strategy to determine a pool\nof candidate initial cluster centers. To find the best initial\ncluster centers from the pool of candidate initial cluster\ncenters, two methods based on quantile strategy and PAM\nstrategy respectively are proposed. During a clustering\nprocess, medoids of clusters are used to update cluster centers\nin each iteration. Comparison between KQM and the method of\nrandomly selecting initial cluster centers shows that KQM is\nalmost always getting clustering results with smaller total sum\nsquares of distances.",
  "Collate": "AllClasses.R KQM.R C.f.R Dist.R",
  "License": "GPL-2",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-21 08:28:34 UTC",
    "User": "root"
  },
  "Author": "Yarong Yang [aut, cre], Nader Ebrahimi [ctb], Yoram Rubin\n[ctb], Jacob Zhang [ctb]",
  "Maintainer": "Yarong Yang <Yi.YA_yaya@hotmail.com>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2025-10-16 18:30:19 UTC",
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    "author": "Yarong Yang <Yi.YA_yaya@hotmail.com>",
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    "message": "version 1.1.1\n",
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    "name": "Yarong Yang",
    "email": "yi.ya_yaya@hotmail.com"
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      "package": "methods",
      "role": "Depends"
    },
    {
      "package": "MASS",
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    "description": "Unofficial read-only mirror of all CRAN R packages"
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    "extra/citation.json",
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    {
      "page": "KQM-package",
      "title": "K Quantiles Medoids (KQM) Clustering.",
      "topics": [
        "KQM-package"
      ]
    },
    {
      "page": "C.f",
      "title": "Finding the center of a cluster.",
      "topics": [
        "C.f"
      ]
    },
    {
      "page": "Dist",
      "title": "Finding the distance between two observations.",
      "topics": [
        "Dist"
      ]
    },
    {
      "page": "KQM",
      "title": "K Quantiles Medoids (KQM) Clustering.",
      "topics": [
        "KQM"
      ]
    },
    {
      "page": "KQMobj-class",
      "title": "Class to contain the results from function KQM.",
      "topics": [
        "KQMobj-class"
      ]
    }
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