Function reference • geocmeans

Clustering methods Functions for applying the proposed fuzzy clustering methods in geocmeans.
`CMeans()`	C-means
`GCMeans()`	Generalized C-means
`SFCMeans()`	SFCMeans
`SGFCMeans()`	SGFCMeans
Parameter selections Functions to help in the selection of clustering parameters.
`select_parameters()` `selectParameters()`	Select parameters for a clustering algorithm
`select_parameters.mc()` `selectParameters.mc()`	Select parameters for clustering algorithm (multicore)
Clustering stability evaluation Functions to investigate the stability of clustering results.
`boot_group_validation()`	Check the robustness of a classification by Bootstrap
`boot_group_validation.mc()`	Check that the obtained groups are stable by bootstrap (multicore)
Clustering quality evaluation Functions to evaluate the quality of clustering results.
`calcCalinskiHarabasz()`	Calinski-Harabasz index
`calcDaviesBouldin()`	Davies-Bouldin index
`calcexplainedInertia()`	Explained inertia index
`calcFukuyamaSugeno()`	Fukuyama and Sugeno index
`calcGD43()`	Generalized Dunn’s index (43)
`calcGD53()`	Generalized Dunn’s index (53)
`calcNegentropyI()`	Negentropy Increment index
`calcSilhouetteIdx()`	Fuzzy Silhouette index
`calcUncertaintyIndex()`	Diversity index
`calcqualityIndexes()`	Quality indexes
Clustering spatial evaluation Functions to evaluate the spatial dimension of clustering results.
`calcELSA()`	calculate ELSA statistic for a hard partition
`calcFuzzyELSA()`	calculate ELSA statistic for a fuzzy partition
`calc_moran_raster()`	Global Moran I for raster
`calc_local_moran_raster()`	Local Moran I for raster
`spatialDiag()`	Spatial diagnostic
`spConsistency()`	Spatial consistency index
`adjustSpatialWeights()`	Semantic adjusted spatial weights
Visualization functions Several functions to visualize clustering results.
`mapClusters()`	Mapping the clusters
`uncertaintyMap()`	Uncertainty map
`spiderPlots()`	Spider chart
`barPlots()`	Bar plots
`violinPlots()`	Violin plots
`sp_clust_explorer()`	Classification result explorer
FCMres S3 methods Several methods to work with FCMres objects.
`is(<FCMres>)`	is method for FCMres
`plot(<FCMres>)`	Plot method for FCMres object
`predict(<FCMres>)`	Predict method for FCMres object
`predict_membership()`	Predict matrix membership for new observations
`print(<FCMres>)`	print method for FCMres
`summary(<FCMres>)`	Summary method for FCMres
`summarizeClusters()`	Descriptive statistics by group
Utility functions Several complementary functions that can be handy.
`cat_to_belongings()` `catToBelongings()`	Convert categories to membership matrix
`groups_matching()`	Match the groups obtained from two classifications
`undecidedUnits()`	Undecided observations
`circular_window()`	Circular window
`standardizer()`	Standardizing helper
Datasets Available datasets in geocmeans.
`LyonIris`	social and environmental indicators for the Iris of the metropolitan region of Lyon (France)
`load_arcachon()`	SpatRaster of the bay of Arcachon

Reference

Clustering methods

Parameter selections

Clustering stability evaluation

Clustering quality evaluation

Clustering spatial evaluation

Visualization functions

FCMres S3 methods

Utility functions

Datasets