kmeans function. It creates
several partitions forming a cascade from a small to a large number of
groups.cascadeKM(data, inf.gr, sup.gr, iter = 100, criterion = "calinski")cIndexKM(y, x, index = "all")
## S3 method for class 'cascadeKM':
plot(x, min.g, max.g, grpmts.plot = TRUE,
sortg = FALSE, gridcol = NA, ...)
"calinski", which refers to
the Calinski-Harabasz (1974) criterion. The simple structure index
("ssi") is also available. Other indice"kmeans" returned by a clustering algorithm
such as kmeansplot"calinski" and "ssi".
Type "all" to obtain both indices.
Abbreviations of these names are also accepted.TRUE or FALSE).x, although not in the graph. INA,
which is the default value, removes the grid lines.kmeans. Most
of the work is performed by function cIndex which is based on the
clustIndex function (package "calinski", which refers to the well-known
Calinski-Harabasz (1974) criterion. The other available index is the
simple structure index "ssi". In the case of groups of equal
sizes, "calinski" is generally a good criterion to indicate the
correct number of groups. Users should not take its indications
literally when the groups are not equal in size. Type "all" to
obtain both indices. The indices are defined as:
[object Object],cascadeKM returns an object of class
cascadeKM with items:
inf.gr to $K$ =
sup.gr. }
Function cIndex returns a vector with the index values. The
maximum value of these indices is supposed to indicate the best
partition. These indices work best with groups of equal sizes. When
the groups are not of equal sizes, one should not put too much faith
in the maximum of these indices, and also explore the groups
corresponding to other values of $K$.
Weingessel, A., Dimitriadou, A. and Dolnicar, S. An Examination
Of Indexes For Determining The Number Of Clusters In Binary Data
Sets,
[object Object],[object Object]