{
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  "Title": "Determination of K Using Peak Counts of Features for Clustering",
  "Version": "1.1.0",
  "Date": "2020-02-08",
  "Authors@R": "c(person(\"Zeynel\", \"Cebeci\", email = \"zcebeci@cukurova.edu.tr\", role = c(\"aut\", \"cre\")), \nperson(\"Cagatay\",\"Cebeci\", role = \"aut\", email = \"cagataycebeci@gmail.com\"))",
  "Author": "Zeynel Cebeci [aut, cre], Cagatay Cebeci [aut]",
  "Maintainer": "Zeynel Cebeci <zcebeci@cukurova.edu.tr>",
  "Description": "The number of clusters (k) is needed to start all the\npartitioning clustering algorithms. An optimal value of this\ninput argument is widely determined by using some internal\nvalidity indices. Since most of the existing internal indices\nsuggest a k value which is computed from the clustering results\nafter several runs of a clustering algorithm they are\ncomputationally expensive. On the contrary, the package\n'kpeaks' enables to estimate k before running any clustering\nalgorithm. It is based on a simple novel technique using the\ndescriptive statistics of peak counts of the features in a data\nset.",
  "License": "GPL (>= 2)",
  "LazyData": "true",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-20 09:15:35 UTC",
    "User": "root"
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2020-02-08 17:00:02 UTC",
  "RemoteUrl": "https://github.com/cran/kpeaks",
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    "name": "Zeynel Cebeci",
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    {
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    {
      "package": "methods",
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    "name": "cran",
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/kpeaks"
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  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/kpeaks.html",
    "extra/NEWS.html",
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    "findpolypeaks",
    "genpolygon",
    "plotpolygon",
    "rmshoulders"
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      "name": "x5p4c",
      "title": "Synthetic Data Set contains 5 Variables and 4 Clusters",
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      "tojson": true
    }
  ],
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    {
      "page": "kpeaks-package",
      "title": "Determination of K Using Peak Counts of Features for Clustering",
      "concept": [
        "finding number of clusters",
        "partitioning-based clustering"
      ],
      "topics": [
        "kpeaks-package"
      ]
    },
    {
      "page": "findk",
      "title": "Estimate the Number of Clusters in a Data Set",
      "concept": [
        "number of clusters",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "findk"
      ]
    },
    {
      "page": "findpolypeaks",
      "title": "Find the Peaks of a Frequency Polygon",
      "concept": [
        "frequency polygons",
        "frequency distribution",
        "peaks in a distribution"
      ],
      "topics": [
        "findpolypeaks"
      ]
    },
    {
      "page": "genpolygon",
      "title": "Generate the Classes to Build a Frequency Polygon",
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        "frequency polygons"
      ],
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        "genpolygon"
      ]
    },
    {
      "page": "plotpolygon",
      "title": "Plot Frequency Polygons",
      "concept": [
        "frequency polygons",
        "histograms",
        "scatter plots"
      ],
      "topics": [
        "plotpolygon"
      ]
    },
    {
      "page": "rmshoulders",
      "title": "Shoulders Removal in Frequency Polygons",
      "concept": [
        "frequency polygons",
        "peaks in a frequency polygon"
      ],
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        "rmshoulders"
      ]
    },
    {
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      "title": "Synthetic Data Set contains 5 Variables and 4 Clusters",
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        "cluster analysis"
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  "_nocasepkg": "kpeaks",
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