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#FAMD (factorial analysis for mixed data) , we retain the first two factors
Salary.afdm <- dudi.mix(Salary,scannf=F,nf=2)
#factorial coordinates for the first 3 instances
print(head(Salary.afdm$li,3))
#affichage des v.p.
plot(round(Salary.afdm$eig,3))
scatter(Salary.afdm,posieig="top",clab.row=0)
#euclidian distance between pairs of instancesdistance euclidenne from the FAMD 2 factors
dist.afdm <- dist(Salary.afdm$li[,1:2],method="euclidian")
#squared distance for Ward's m?thod
#see http://en.wikipedia.org/wiki/Ward's_method
dist.afdm <- dist.afdm^2
#HAC from the distance matrix
Salary.tree <- hclust(dist.afdm,method="ward")
plot(Salary.tree)
#cutting in 8 groupes
Salary.clusters <- cutree(Salary.tree,k=6)
table(Salary.clusters) |
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