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.