GCMeans {geocmeans} | R Documentation |
The generalized c-mean algorithm
GCMeans( data, k, m, beta, maxiter = 500, tol = 0.01, standardize = TRUE, verbose = TRUE, init = "random", seed = NULL )
data |
A dataframe with only numerical variable |
k |
An integer describing the number of cluster to find |
m |
A float for the fuzziness degree |
beta |
A float for the beta parameter (control speed convergence and classification crispness) |
maxiter |
A float for the maximum number of iteration |
tol |
The tolerance criterion used in the evaluateMatrices function for convergence assessment |
standardize |
A boolean to specify if the variables must be centered and reduced (default = True) |
verbose |
A boolean to specify if the messages should be displayed |
init |
A string indicating how the initial centers must be selected. "random" indicates that random observations are used as centers. "kpp" use a distance based method resulting in more dispersed centers at the beginning. Both of them are heuristic. |
seed |
An integer used for random number generation. It ensures that the start centers will be the same if the same integer is selected. |
A named list with :
Centers: a dataframe describing the final centers of the groups
Belongings: the final membership matrix
Groups: a vector with the names of the most likely group for each observation
Data: the dataset used to perform the clustering (might be standardized)
data(LyonIris) AnalysisFields <-c("Lden","NO2","PM25","VegHautPrt","Pct0_14","Pct_65","Pct_Img", "TxChom1564","Pct_brevet","NivVieMed") dataset <- LyonIris@data[AnalysisFields] result <- GCMeans(dataset,k = 5, m = 1.5, beta = 0.5, standardize = TRUE)