Partition Fuzziness
Compute the fuzziness of partitions.
cl_fuzziness(x, method = NULL, normalize = TRUE)
x |
a cluster ensemble of partitions, or an R object coercible to such. |
method |
a character string indicating the fuzziness measure to
be employed, or |
normalize |
a logical indicating whether the fuzziness measure should be normalized in a way that hard partitions have value 0, and “completely fuzzy” partitions (where for all objects, all classes get the same membership) have value 1. |
If m contains the membership values of a partition, the (unnormalized) Partition Coefficient and Partition Entropy are given by ∑_{n,i} m_{n,i}^2 and ∑_{n,i} H(m_{n,i}), respectively, where u log(u) - (1-u) log(1-u).
Note that the normalization used here is different from the normalizations typically found in the literature.
If a user-defined fuzziness method is to be employed, is must be a function taking a matrix of membership values and a logical to indicate whether normalization is to be performed as its arguments (in that order; argument names are not used).
An object of class "cl_fuzziness" giving the fuzziness
values.
J. C. Bezdek (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum.
Function fclustIndex in package e1071,
which also computes several other “fuzzy cluster indexes”
(typically based on more information than just the membership
values).
if(require("e1071", quietly = TRUE)) {
## Use an on-line version of fuzzy c-means from package e1071 if
## available.
data("Cassini")
pens <- cl_boot(Cassini$x, B = 15, k = 3, algorithm = "cmeans",
parameters = list(method = "ufcl"))
pens
summary(cl_fuzziness(pens, "PC"))
summary(cl_fuzziness(pens, "PE"))
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.