fclustIndex {e1071} | R Documentation |
Calculates the values of several fuzzy validity measures. The values of the indexes can be independently used in order to evaluate and compare clustering partitions or even to determine the number of clusters existing in a data set.
fclustIndex(y, x, index = "all")
y |
An object of a fuzzy clustering result of class "fclust" |
x |
Data matrix |
index |
The validity measures used: "gath.geva" , "xie.beni" ,
"fukuyama.sugeno" , "partition.coefficient" ,
"partition.entropy" , "proportion.exponent" ,
"separation.index" and "all" for all the indexes. |
The validity measures and a short description of them follows, where N is the number of data points, u_{ij} the values of the membership matrix, v_j the centers of the clusters and k te number of clusters.
Returns a vector with the validity measures values.
Evgenia Dimitriadou
James C. Bezdek, Pattern Recognition with Fuzzy Objective
Function Algorithms, Plenum Press, 1981, NY.
L. X. Xie and G. Beni, Validity measure for fuzzy
clustering, IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 3, n. 8, p. 841-847, 1991.
I. Gath and A. B. Geva, Unsupervised Optimal Fuzzy
Clustering, IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 11, n. 7, p. 773-781, 1989.
Y. Fukuyama and M. Sugeno, A new method of choosing the
number of clusters for the fuzzy $c$-means method, Proc. 5th Fuzzy
Syst. Symp., p. 247-250, 1989 (in japanese).
# a 2-dimensional example x<-rbind(matrix(rnorm(100,sd=0.3),ncol=2), matrix(rnorm(100,mean=1,sd=0.3),ncol=2)) cl<-cmeans(x,2,20,verbose=TRUE,method="cmeans") resultindexes <- fclustIndex(cl,x, index="all") resultindexes