| cshell {e1071} | R Documentation |
The c-shell clustering algorithm, the shell prototype-based version (ring prototypes) of the fuzzy kmeans clustering method.
cshell(x, centers, iter.max=100, verbose=FALSE, dist="euclidean",
method="cshell", m=2, radius = NULL)
x |
The data matrix, were columns correspond to the variables and rows to observations. |
centers |
Number of clusters or initial values for cluster centers |
iter.max |
Maximum number of iterations |
verbose |
If TRUE, make some output during learning |
dist |
Must be one of the following: If "euclidean", the
mean square error, if "manhattan", the mean absolute error is
computed. Abbreviations are also accepted. |
method |
Currently, only the "cshell" method; the c-shell fuzzy
clustering method |
m |
The degree of fuzzification. It is defined for values greater than 1 |
radius |
The radius of resulting clusters |
The data given by x is clustered by the fuzzy c-shell algorithm.
If centers is a matrix, its rows are taken as the initial cluster
centers. If centers is an integer, centers rows
of x are randomly chosen as initial values.
The algorithm stops when the maximum number of iterations (given by
iter.max) is reached.
If verbose is TRUE, it displays for each iteration the number
the value of the objective function.
If dist is "euclidean", the distance between the
cluster center and the data points is the Euclidean distance (ordinary
kmeans algorithm). If "manhattan", the distance between the
cluster center and the data points is the sum of the absolute values
of the distances of the coordinates.
If method is "cshell", then we have the c-shell
fuzzy clustering method.
The parameters m defines the degree of fuzzification. It is
defined for real values greater than 1 and the bigger it is the more
fuzzy the membership values of the clustered data points are.
The parameter radius is by default set to 0.2 for every
cluster.
cshell returns an object of class "cshell".
centers |
The final cluster centers. |
size |
The number of data points in each cluster. |
cluster |
Vector containing the indices of the clusters where the data points are assigned to. The maximum membership value of a point is considered for partitioning it to a cluster. |
iter |
The number of iterations performed. |
membership |
a matrix with the membership values of the data points to the clusters. |
withinerror |
Returns the sum of square distances within the clusters. |
call |
Returns a call in which all of the arguments are specified by their names. |
Evgenia Dimitriadou
Rajesh N. Dave. Fuzzy Shell-Clustering and Applications to Circle Detection in Digital Images. Int. J. of General Systems, Vol. 16, pp. 343-355, 1996.
## a 2-dimensional example
x<-rbind(matrix(rnorm(50,sd=0.3),ncol=2),
matrix(rnorm(50,mean=1,sd=0.3),ncol=2))
cl<-cshell(x,2,20,verbose=TRUE,method="cshell",m=2)
print(cl)
# assign classes to some new data
y<-rbind(matrix(rnorm(13,sd=0.3),ncol=2),
matrix(rnorm(13,mean=1,sd=0.3),ncol=2))
# ycl<-predict(cl, y, type="both")