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:
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 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.
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 neighbor cluster of i (the cluster,
not containing i, for which the average dissimilarity between its observations
and i is minimal), and the silhouette width of i.
The other two components give the average silhouette width per cluster and
the average silhouette width for the dataset.
See
plot.partition for more information.
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.