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
object 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 objects. 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 objects, giving for each object 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.
The first component is a matrix, with for each object 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 objects 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.
|
fanny
.
"fanny"
class has methods for the following generic functions:
print
, summary
.
"fanny"
inherits from "partition"
.
By that, the generic function plot
can be used with a fanny
object.
fanny
object is a list with the following components:
fanny
, partition.object
, plot.partition
, dissimilarity.object
.