{
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  "Package": "inaparc",
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  "Title": "Initialization Algorithms for Partitioning Cluster Analysis",
  "Version": "1.2.1",
  "Date": "2025-09-07",
  "Authors@R": "c(person(\"Zeynel\", \"Cebeci\", email = \"zcebeci@cu.edu.tr\", role = c(\"aut\", \"cre\")), \nperson(\"Cagatay\",\"Cebeci\", role = \"aut\", email = \"cebecicagatay@gmail.com\"))",
  "Author": "Zeynel Cebeci [aut, cre], Cagatay Cebeci [aut]",
  "Maintainer": "Zeynel Cebeci <zcebeci@cu.edu.tr>",
  "Description": "Partitioning clustering algorithms divide data sets into k\nsubsets or partitions so-called clusters. They require some\ninitialization procedures for starting the algorithms.\nInitialization of cluster prototypes is one of such kind of\nprocedures for most of the partitioning algorithms. Cluster\nprototypes are the centers of clusters, i.e. centroids or\nmedoids, representing the clusters in a data set. In order to\ninitialize cluster prototypes, the package 'inaparc' contains a\nset of the functions that are the implementations of several\nlinear time-complexity and loglinear time-complexity methods in\naddition to some novel techniques. Initialization of fuzzy\nmembership degrees matrices is another important task for\nstarting the probabilistic and possibilistic partitioning\nalgorithms. In order to initialize membership degrees matrices\nrequired by these algorithms, a number of functions based on\nsome traditional and novel initialization techniques are also\navailable in the package 'inaparc'.",
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      "page": "inaparc-package",
      "title": "Initialization Algorithms for Partitioning Cluster Analysis",
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        "initialization of cluster prototypes",
        "cluster seeding techniques",
        "initialization of membership degrees matrix",
        "prototype-based clustering",
        "partitioning clustering",
        "partitional clustering",
        "non-hierarchial clustering",
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        "unsupervised learning"
      ],
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      ]
    },
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      "title": "Initialization of cluster prototypes using Al-Daoud's algorithm",
      "concept": [
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        "sampling for prototype selection",
        "prototype-based clustering",
        "partitional clustering",
        "cluster analysis",
        "unsupervised learning"
      ],
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      "title": "Initialization of cluster prototypes using Ball & Hall's algorithm",
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        "prototype-based clustering",
        "partitional clustering",
        "cluster analysis",
        "unsupervised learning"
      ],
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    {
      "page": "crsamp",
      "title": "Initialization of cluster prototypes using the centers of random samples",
      "concept": [
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        "prototype-based clustering",
        "partitional clustering",
        "cluster analysis",
        "unsupervised learning"
      ],
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    },
    {
      "page": "figen",
      "title": "Initialization of membership degrees over class range of a selected feature",
      "concept": [
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        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis",
        "unsupervised learning"
      ],
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    },
    {
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      "concept": [
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        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis",
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        "sampling for prototype selection",
        "prototype-based clustering",
        "partitional clustering",
        "cluster analysis",
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      ],
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    },
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      "title": "Get the names of algorithms in 'inaparc'",
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      ]
    },
    {
      "page": "hartiganwong",
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      "concept": [
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        "sampling for prototype selection",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis",
        "unsupervised learning"
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    {
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        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis",
        "unsupervised learning"
      ],
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        "partitioning clustering",
        "cluster analysis",
        "unsupervised learning"
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        "cluster analysis",
        "unsupervised learning"
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    },
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        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis",
        "unsupervised learning"
      ],
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    },
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      "title": "Initialization of cluster prototypes using Insdev algorithm",
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        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis",
        "unsupervised learning"
      ],
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        "partitioning clustering",
        "cluster analysis",
        "unsupervised learning"
      ],
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      "title": "Initialization of cluster prototypes using K-means++ algorithm",
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        "initialization of cluster prototypes",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis",
        "unsupervised learning"
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    {
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      "title": "Initialization of cluster prototypes using the centers of <k> segments",
      "concept": [
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        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis",
        "unsupervised learning"
      ],
      "topics": [
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      "title": "Initialization of cluster prototypes using the centers of <k> blocks",
      "concept": [
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        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis",
        "unsupervised learning"
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      "concept": [
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    {
      "page": "lhsmaximin",
      "title": "Initialization of cluster prototypes using Maximin LHS",
      "concept": [
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        "sampling for prototype selection",
        "prototype-based clustering",
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