calcqualityIndexes {geocmeans} | R Documentation |
calculate several clustering quality indexes (most of them come from fclust package)
calcqualityIndexes(data, belongmatrix, m)
data |
The original dataframe used for the classification (n*p) |
belongmatrix |
A membership matrix (n*k) |
m |
The fuzziness parameter used for the classification |
A named list with
Silhouette.index: the silhouette index (fclust::SIL.F)
Partition.entropy: the partition entropy index (fclust::PE)
Partition.coeff: the partition entropy coefficient (fclust::PC)
Modified.partition.coeff: the modified partition entropy coefficient (fclust::MPC)
XieBeni.index : the Xie and Beni index (fclust::XB)
FukuyamaSugeno.index : the Fukuyama and Sugeno index (geocmeans::calcFukuyamaSugeno)
Explained.inertia: the percentage of total inertia explained by the solution
data(LyonIris) AnalysisFields <-c("Lden","NO2","PM25","VegHautPrt","Pct0_14","Pct_65","Pct_Img", "TxChom1564","Pct_brevet","NivVieMed") dataset <- LyonIris@data[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)