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  "Title": "Implementing Methods for Spatial Fuzzy Unsupervised\nClassification",
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  "Authors@R": "c(\nperson(\"Jeremy\", \"Gelb\", email = \"jeremy.gelb@ucs.inrs.ca\",role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-7114-2714\")),\nperson(\"Philippe\", \"Apparicio\", email=\"philippe.apparicio@ucs.inrs.ca\", role=c(\"ctb\"), comment = c(ORCID = \"0000-0001-6466-9342\"))\n)",
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  "Description": "Provides functions to apply spatial fuzzy unsupervised\nclassification, visualize and interpret results. This method is\nwell suited when the user wants to analyze data with a fuzzy\nclustering algorithm and to account for the spatial dimension\nof the dataset. In addition, indexes for estimating the spatial\nconsistency and classification quality are proposed. The\nmethods were originally proposed in the field of brain imagery\n(seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and\nZaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently\napplied in geography (see Gelb and Apparicio\n<doi:10.4000/cybergeo.36414>).",
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  "Date/Publication": "2026-04-19 23:44:09 UTC",
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    "calcQualIdx",
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    "calcRobustSigmas",
    "calcSFCMBelongMatrix",
    "calcSFCMBelongMatrixNoisy",
    "calcSFGCMBelongMatrix",
    "calcSFGCMBelongMatrixNoisy",
    "calcSilhouetteIdx",
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    "centersGFCM",
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    "elsa_fuzzy_vector",
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      "page": "Arcachon",
      "title": "SpatRaster of the bay of Arcachon",
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      "page": "barPlots",
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      "title": "Check the robustness of a classification by Bootstrap",
      "topics": [
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      "title": "Check that the obtained groups are stable by bootstrap (multicore)",
      "topics": [
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      "page": "calc_local_moran_raster",
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      "topics": [
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    {
      "page": "calc_moran_raster",
      "title": "Global Moran I for raster",
      "topics": [
        "calc_moran_raster"
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      "page": "calcCalinskiHarabasz",
      "title": "Calinski-Harabasz index",
      "topics": [
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      "title": "Davies-Bouldin index",
      "topics": [
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    {
      "page": "calcELSA",
      "title": "calculate ELSA statistic for a hard partition",
      "topics": [
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    {
      "page": "calcexplainedInertia",
      "title": "Explained inertia index",
      "topics": [
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      "page": "calcFukuyamaSugeno",
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    {
      "page": "calcFuzzyELSA",
      "title": "calculate ELSA statistic for a fuzzy partition",
      "topics": [
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      "page": "calcGD43",
      "title": "Generalized Dunn’s index (43)",
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      "page": "calcGD53",
      "title": "Generalized Dunn’s index (53)",
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    },
    {
      "page": "calcNegentropyI",
      "title": "Negentropy Increment index",
      "topics": [
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    {
      "page": "calcqualityIndexes",
      "title": "Quality indexes",
      "topics": [
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    },
    {
      "page": "calcSilhouetteIdx",
      "title": "Fuzzy Silhouette index",
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    },
    {
      "page": "calcUncertaintyIndex",
      "title": "Diversity index",
      "topics": [
        "calcUncertaintyIndex"
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    },
    {
      "page": "cat_to_belongings",
      "title": "Convert categories to membership matrix",
      "topics": [
        "catToBelongings",
        "cat_to_belongings"
      ]
    },
    {
      "page": "circular_window",
      "title": "Circular window",
      "topics": [
        "circular_window"
      ]
    },
    {
      "page": "CMeans",
      "title": "C-means",
      "topics": [
        "CMeans"
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    },
    {
      "page": "GCMeans",
      "title": "Generalized C-means",
      "topics": [
        "GCMeans"
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    },
    {
      "page": "groups_matching",
      "title": "Match the groups obtained from two classifications",
      "topics": [
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    {
      "page": "is.FCMres",
      "title": "is method for FCMres",
      "topics": [
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    {
      "page": "LyonIris",
      "title": "social and environmental indicators for the Iris of the metropolitan region of Lyon (France)",
      "topics": [
        "LyonIris"
      ]
    },
    {
      "page": "mapClusters",
      "title": "Mapping the clusters",
      "topics": [
        "mapClusters"
      ]
    },
    {
      "page": "plot.FCMres",
      "title": "Plot method for FCMres object",
      "topics": [
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    },
    {
      "page": "predict_membership",
      "title": "Predict matrix membership for new observations",
      "topics": [
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    },
    {
      "page": "predict.FCMres",
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      "title": "Spatial Fuzzy CMean with R",
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        "Loading the packages and the data",
        "Classical Kmeans",
        "Classical c-means and generalized c-means",
        "Spatial c-means and generalized c-means",
        "Selecting alpha for SFCM",
        "Selecting alpha and beta for SGFCM",
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