{
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  "Package": "ppclust",
  "Type": "Package",
  "Title": "Probabilistic and Possibilistic Cluster Analysis",
  "Version": "1.1.0.1",
  "Date": "2020-02-08",
  "Encoding": "UTF-8",
  "Authors@R": "c(person(\"Zeynel\", \"Cebeci\", email = \"zcebeci@cukurova.edu.tr\", role = c(\"aut\", \"cre\")), \nperson(\"Figen\",\"Yildiz\", role = \"aut\", email = \"yildizf@cukurova.edu.tr\"),\nperson(\"Alper Tuna\",\"Kavlak\", role = \"aut\", email = \"alpertunakavlak@gmail.com\"),\nperson(\"Cagatay\",\"Cebeci\", role = \"aut\", email = \"cagatay.cebeci@strath.ac.uk\"),\nperson(\"Hasan\",\"Onder\", role = \"aut\", email = \"honder@omu.edu.tr\"))",
  "Author": "Zeynel Cebeci [aut, cre], Figen Yildiz [aut], Alper Tuna Kavlak\n[aut], Cagatay Cebeci [aut], Hasan Onder [aut]",
  "Maintainer": "Zeynel Cebeci <zcebeci@cukurova.edu.tr>",
  "Description": "Partitioning clustering divides the objects in a data set\ninto non-overlapping subsets or clusters by using the\nprototype-based probabilistic and possibilistic clustering\nalgorithms. This package covers a set of the functions for\nFuzzy C-Means (Bezdek, 1974) <doi:10.1080/01969727308546047>,\nPossibilistic C-Means (Krishnapuram & Keller, 1993)\n<doi:10.1109/91.227387>, Possibilistic Fuzzy C-Means (Pal et\nal, 2005) <doi:10.1109/TFUZZ.2004.840099>, Possibilistic\nClustering Algorithm (Yang et al, 2006)\n<doi:10.1016/j.patcog.2005.07.005>, Possibilistic C-Means with\nRepulsion (Wachs et al, 2006) <doi:10.1007/3-540-31662-0_6> and\nthe other variants of hard and soft clustering algorithms. The\ncluster prototypes and membership matrices required by these\npartitioning algorithms are initialized with different\ninitialization techniques that are available in the package\n'inaparc'. As the distance metrics, not only the Euclidean\ndistance but also a set of the commonly used distance metrics\nare available to use with some of the algorithms in the\npackage.",
  "License": "GPL (>= 2)",
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  "VignetteBuilder": "knitr, rmarkdown",
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2023-12-14 02:39:04 UTC",
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    "fcm",
    "fcm2",
    "fpcm",
    "fpppcm",
    "get.dmetrics",
    "gg",
    "gk",
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    "hcm",
    "is.ppclust",
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    "pca",
    "pcm",
    "pcmr",
    "pfcm",
    "plotcluster",
    "ppclust2",
    "summary.ppclust",
    "upfc"
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      "title": "Synthetic data set of two variables",
      "object": "x12",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "p1",
        "p2"
      ],
      "rows": 12,
      "table": true,
      "tojson": true
    },
    {
      "name": "x16",
      "title": "Synthetic data set of two variables forming two clusters",
      "object": "x16",
      "class": [
        "data.frame"
      ],
      "fields": [
        "p1",
        "p2",
        "cl"
      ],
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      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "ppclust-package",
      "title": "Probabilistic and Possibilistic Cluster Analysis",
      "concept": [
        "fuzzy clustering",
        "crisp clustering",
        "hard clustering",
        "probabilistic clustering",
        "possibilistic clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "partitional clustering",
        "cluster analysis",
        "unsupervised learning",
        "flat clustering",
        "non-hiearchical clustering"
      ],
      "topics": [
        "ppclust-package"
      ]
    },
    {
      "page": "as.ppclust",
      "title": "Convert object to 'ppclust' class",
      "topics": [
        "as.ppclust"
      ]
    },
    {
      "page": "comp.omega",
      "title": "Compute the possibilistic penalty argument for PCM",
      "concept": [
        "possibilistic c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "comp.omega"
      ]
    },
    {
      "page": "crisp",
      "title": "Crisp the fuzzy membership degrees",
      "concept": [
        "fuzzy c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "crisp"
      ]
    },
    {
      "page": "ekm",
      "title": "K-Means Clustering Using Different Seeding Techniques",
      "concept": [
        "hard c-means clustering",
        "k-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "ekm"
      ]
    },
    {
      "page": "fcm",
      "title": "Fuzzy C-Means Clustering",
      "concept": [
        "fuzzy c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "fcm"
      ]
    },
    {
      "page": "fcm2",
      "title": "Type-2 Fuzzy C-Means Clustering",
      "concept": [
        "type-2 fuzzy c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "fcm2"
      ]
    },
    {
      "page": "fpcm",
      "title": "Fuzzy Possibilistic C-Means Clustering",
      "concept": [
        "fuzzy possibilistic c-means clustering",
        "possibilistic c-means clustering",
        "mixed c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "fpcm"
      ]
    },
    {
      "page": "fpppcm",
      "title": "Fuzzy Possibilistic Product Partition C-Means Clustering",
      "concept": [
        "probabilistic clustering",
        "possibilistic clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "fpppcm"
      ]
    },
    {
      "page": "get.dmetrics",
      "title": "List the names of distance metrics",
      "concept": [
        "distance",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "get.dmetrics"
      ]
    },
    {
      "page": "gg",
      "title": "Gath-Geva Clustering Algorithm",
      "concept": [
        "fuzzy clustering",
        "gath-geva clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "gg"
      ]
    },
    {
      "page": "gk",
      "title": "Gustafson-Kessel Clustering",
      "concept": [
        "fuzzy clustering",
        "gustafson-kessel clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "gk"
      ]
    },
    {
      "page": "gkpfcm",
      "title": "Gustafson-Kessel Clustering Using PFCM",
      "concept": [
        "fuzzy c-means clustering",
        "gustafson-kessel clustering",
        "mixed c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "gkpfcm"
      ]
    },
    {
      "page": "hcm",
      "title": "Hard C-Means Clustering",
      "concept": [
        "hard c-means clustering",
        "k-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "hcm"
      ]
    },
    {
      "page": "is.ppclust",
      "title": "Check the class of object for 'ppclust'",
      "topics": [
        "is.ppclust"
      ]
    },
    {
      "page": "mfpcm",
      "title": "Modified Fuzzy Possibilistic C-Means Clustering",
      "concept": [
        "fuzzy c-means clustering",
        "possibilistic c-means clustering",
        "mixed c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "mfpcm"
      ]
    },
    {
      "page": "pca",
      "title": "Possibilistic Clustering Algorithm",
      "concept": [
        "possibilistic c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "pca"
      ]
    },
    {
      "page": "pcm",
      "title": "Possibilistic C-Means Clustering",
      "concept": [
        "possibilistic c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "pcm"
      ]
    },
    {
      "page": "pcmr",
      "title": "Possibilistic C-Means Clustering with Repulsion",
      "concept": [
        "possibilistic c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
        "pcmr"
      ]
    },
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      "page": "pfcm",
      "title": "Possibilistic Fuzzy C-Means Clustering Algorithm",
      "concept": [
        "probabilistic clustering",
        "possibilistic clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
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    },
    {
      "page": "plotcluster",
      "title": "Plot Clustering Results",
      "concept": [
        "possibilistic c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
      "topics": [
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    },
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      "page": "ppclust2",
      "title": "Convert 'ppclust' objects to the other types of cluster objects",
      "topics": [
        "ppclust2"
      ]
    },
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      "title": "Summarize the clustering results",
      "concept": [
        "possibilistic c-means clustering",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
      ],
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      "title": "Unsupervised Possibilistic Fuzzy C-Means Clustering Algorithm",
      "concept": [
        "probabilistic clustering",
        "possibilistic clustering",
        "unsupervised learning",
        "prototype-based clustering",
        "partitioning clustering",
        "cluster analysis"
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        "PREPARING FOR THE ANALYSIS",
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        "Pairwise Scatter Plots",
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        "Typicality Degrees Matrix",
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        "Summary of Clustering Results",
        "Run UPFC with Multiple Starts",
        "Display the best solution",
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        "VISUALIZATION OF THE CLUSTERING RESULTS",
        "Pairwise Scatter Plots",
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        "VALIDATION OF THE CLUSTERING RESULTS",
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      "modified": "2020-02-09 09:30:02",
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