| CMeans {geocmeans} | R Documentation |
C-means
Description
The classical c-mean algorithm
Usage
CMeans(
data,
k,
m,
maxiter = 500,
tol = 0.01,
standardize = TRUE,
robust = FALSE,
noise_cluster = FALSE,
delta = NULL,
verbose = TRUE,
init = "random",
seed = NULL
)
Arguments
data |
A dataframe with only numerical variables. Can also be a list of rasters (produced by the package raster). In that case, each raster is considered as a variable and each pixel is an observation. Pixels with NA values are not used during the classification. |
k |
An integer describing the number of cluster to find |
m |
A float for the fuzziness degree |
maxiter |
An integer for the maximum number of iterations |
tol |
The tolerance criterion used in the evaluateMatrices function for convergence assessment |
standardize |
A boolean to specify if the variables must be centred and reduced (default = True) |
robust |
A boolean indicating if the "robust" version of the algorithm must be used (see details) |
noise_cluster |
A boolean indicatong if a noise cluster must be added to the solution (see details) |
delta |
A float giving the distance of the noise cluster to each observation |
verbose |
A boolean to specify if the progress should be printed |
init |
A string indicating how the initial centres must be selected. "random" indicates that random observations are used as centres. "kpp" use a distance-based method resulting in more dispersed centres at the beginning. Both of them are heuristic. |
seed |
An integer used for random number generation. It ensures that the starting centres will be the same if the same value is selected. |
Value
An S3 object of class FCMres with the following slots
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)
isRaster: TRUE if rasters were used as input data, FALSE otherwise
k: the number of groups
m: the fuzyness degree
alpha: the spatial weighting parameter (if SFCM or SGFCM)
beta: beta parameter for generalized version of FCM (GFCM or SGFCM)
algo: the name of the algorithm used
rasters: a list of rasters with membership values and the most likely group (if rasters were used)
missing: a boolean vector indicating raster cell with data (TRUE) and with NA (FALSE) (if rasters were used)
maxiter: the maximum number of iterations used
tol: the convergence criterio
lag_method: the lag function used (if SFCM or SGFCM)
nblistw: the neighbours list used (if vector data were used for SFCM or SGFCM)
window: the window used (if raster data were used for SFCM or SGFCM)
Examples
data(LyonIris)
AnalysisFields <-c("Lden","NO2","PM25","VegHautPrt","Pct0_14","Pct_65","Pct_Img",
"TxChom1564","Pct_brevet","NivVieMed")
dataset <- sf::st_drop_geometry(LyonIris[AnalysisFields])
result <- CMeans(dataset,k = 5, m = 1.5, standardize = TRUE)