calcqualityIndexes: Quality indexes

In geocmeans: Implementing Methods for Spatial Fuzzy Unsupervised Classification

Quality indexes

Description

calculate several clustering quality indexes (some of them come from fclust package)

Usage

calcqualityIndexes(
  data,
  belongmatrix,
  m,
  indices = c("Silhouette.index", "Partition.entropy", "Partition.coeff",
    "XieBeni.index", "FukuyamaSugeno.index", "Explained.inertia")
)

Arguments

data	The original dataframe used for the classification (n*p)
belongmatrix	A membership matrix (n*k)
m	The fuzziness parameter used for the classification
indices	A character vector with the names of the indices to calculate, default is : c("Silhouette.index", "Partition.entropy", "Partition.coeff", "XieBeni.index", "FukuyamaSugeno.index", "Explained.inertia"). Other available indices are : "DaviesBoulin.index", "CalinskiHarabasz.index", "GD43.index", "GD53.index" and "Negentropy.index"

Value

A named list with with the values of the required indices

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])
queen <- spdep::poly2nb(LyonIris,queen=TRUE)
Wqueen <- spdep::nb2listw(queen,style="W")
result <- SFCMeans(dataset, Wqueen,k = 5, m = 1.5, alpha = 1.5, standardize = TRUE)
calcqualityIndexes(result$Data,result$Belongings, m=1.5)

geocmeans documentation built on Sept. 12, 2023, 9:06 a.m.