Compute fuzzy evaluation statistics
Computes several evaluation statistics on the fuzzy clustering results on objects of class vegclust.
vegclustIndex(y)
y |
An object of class |
These statistics were conceived to be computed on fuzzy partitions, such as the ones coming from Fuzzy C-means (Bezdek 1981). Maximum values of PCN or minimum values of PEN can be used as criteria to choose the number of clusters.
Returns an vector of four values: partition coefficient (PC), normalized partition coefficient (PCN), partition entropy (PE) and normalized partition entropy (PEN).
Miquel De Cáceres, Forest Science Center of Catalonia
Bezdek, J. C. (1981) Pattern recognition with fuzzy objective functions. Plenum Press, New York.
## Loads data
data(wetland)
## This equals the chord transformation
## (see also \code{\link{decostand}} in package vegan)
wetland.chord = as.data.frame(sweep(as.matrix(wetland), 1,
sqrt(rowSums(as.matrix(wetland)^2)), "/"))
## Create noise clustering with 2, 3 and 4 clusters. Perform 10 starts from random seeds
## and keep the best solutions
wetland.fcm2 = vegclust(wetland.chord, mobileCenters=2, m = 1.2, method="FCM", nstart=10)
wetland.fcm3 = vegclust(wetland.chord, mobileCenters=3, m = 1.2, method="FCM", nstart=10)
wetland.fcm4 = vegclust(wetland.chord, mobileCenters=4, m = 1.2, method="FCM", nstart=10)
## Compute statistics. Both PCN and PEN indicate that three groups are more advisable
## than 2 or 4.
print(vegclustIndex(wetland.fcm2))
print(vegclustIndex(wetland.fcm3))
print(vegclustIndex(wetland.fcm4))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.