Arguments
objective
the objective function and the number of iterations the fanny algorithm
needed to reach this minimal value.
membership
matrix containing the memberships for each pair consisting of an
observation and a cluster.
coeff
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 always between 1/k and 1.
The normalized form of the coeffi
clustering
the clustering vector of the nearest crisp clustering. A vector with length
equal to the number of observations, giving for each observation the number
of the cluster to which it has the largest membership.
silinfo
list with all information necessary to construct a silhouette plot of the
nearest crisp clustering. This list is only available when 1 < k < n.
The first component is a matrix, with for each observation i the cluster to
which i belongs, as well as the ne
diss
an object of class "dissimilarity", representing the total dissimilarity
matrix of the dataset.
data
a matrix containing the original or standardized measurements, depending
on the stand option of the function fanny. If a dissimilarity matrix was
given as input structure, then this component is not available.
GENERATION
This class of objects is returned from fanny.METHODS
The "fanny" class has methods for the following generic functions:
print, summary.INHERITANCE
The class "fanny" inherits from "partition".
Therefore, the generic functions plot and clusplot can be used on a
fanny object.STRUCTURE
A legitimate fanny object is a list with the following components: