calcFukuyamaSugeno {geocmeans} | R Documentation |
Fukuyama and Sugeno index
Description
Calculate Fukuyama and Sugeno index of clustering quality
Usage
calcFukuyamaSugeno(data, belongmatrix, centers, m)
Arguments
data |
The original dataframe used for the clustering (n*p) |
belongmatrix |
A membership matrix (n*k) |
centers |
The centres of the clusters |
m |
The fuzziness parameter |
Details
The Fukuyama and Sugeno index (Fukuyama 1989) is the difference between the compacity of clusters and the separation of clusters. A smaller value indicates a better clustering. The formula is:
with n the number of observations, k the number of clusters and the mean of the dataset.
Value
A float: the Fukuyama and Sugeno index
References
Fukuyama Y (1989). “A new method of choosing the number of clusters for the fuzzy c-mean method.” In Proc. 5th Fuzzy Syst. Symp., 1989, 247–250.
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)
calcFukuyamaSugeno(result$Data,result$Belongings, result$Centers, 1.5)
[Package geocmeans version 0.3.4 Index]