# Compute fuzzy evaluation statistics

### Description

Computes several evaluation statistics on the fuzzy clustering results on objects of class `vegclust`

.

### Usage

1 |

### Arguments

`y` |
An object of class |

### Details

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.

### Value

Returns an vector of four values: partition coefficient (PC), normalized partition coefficient (PCN), partition entropy (PE) and normalized partition entropy (PEN).

### Author(s)

Miquel De Cáceres, Forest Science Center of Catalonia

### References

Bezdek, J. C. (1981) Pattern recognition with fuzzy objective functions. Plenum Press, New York.

### See Also

`cmeans`

,`vegclust`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
## 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))
``` |