The objects of class "fanny" represent a fuzzy clustering of a
dataset.
A legitimate fanny object is a list with the following components:
matrix containing the memberships for each pair consisting of an observation and a cluster.
the membership exponent used in the fitting criterion.
Dunn's partition coefficient \(F(k)\) of the clustering, where \(k\) is the number of clusters. \(F(k)\) is the sum of all squared membership coefficients, divided by the number of observations. Its value is between \(1/k\) and 1.
The normalized form of the coefficient is also given. It is defined as \((F(k) - 1/k) / (1 - 1/k)\), and ranges between 0 and 1. A low value of Dunn's coefficient indicates a very fuzzy clustering, whereas a value close to 1 indicates a near-crisp clustering.
the clustering vector of the nearest crisp clustering, see
partition.object.
integer (\(\le k\)) giving the number of crisp
clusters; can be less than \(k\), where it's recommended to
decrease memb.exp.
named vector containing the minimal value of the objective function
reached by the FANNY algorithm and the relative convergence
tolerance tol used.
named vector with iterations, the number of iterations needed
and converged indicating if the algorithm converged (in
maxit iterations within convergence tolerance tol).
an object of class "dissimilarity", see
partition.object.
generating call, see partition.object.
list with silhouette information of the nearest crisp clustering, see
partition.object.
matrix, possibibly standardized, or NULL, see
partition.object.
These objects are returned from fanny.
The "fanny" class has methods for the following generic functions:
print, summary.
The class "fanny" inherits from "partition".
Therefore, the generic functions plot and clusplot can
be used on a fanny object.
fanny, print.fanny,
dissimilarity.object,
partition.object, plot.partition.