Perform k-centroids clustering on a data matrix.
kcca(x, k, family=kccaFamily("kmeans"), weights=NULL, group=NULL,
control=NULL, simple=FALSE, save.data=FALSE)
kccaFamily(which=NULL, dist=NULL, cent=NULL, name=which,
preproc = NULL, trim=0, groupFun = "minSumClusters")# S4 method for kccasimple
summary(object)
A numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a data frame with all numeric columns).
Either the number of clusters, or a vector of cluster
assignments, or a matrix of initial
(distinct) cluster centroids. If a number, a random set of (distinct)
rows in x is chosen as the initial centroids.
Object of class "kccaFamily".
An optional vector of weights to be used in the clustering process, cannot be combined with all families.
An optional grouping vector for the data, see details below.
An object of class "flexclustControl".
Return an object of class "kccasimple"?
Save a copy of x in the return object?
One of "kmeans", "kmedians",
"angle", "jaccard", or "ejaccard".
Optional long name for family, used only for show methods.
A function for distance computation, ignored
if which is specified.
A function for centroid computation, ignored
if which is specified.
Function for data preprocessing.
A number in between 0 and 0.5, if non-zero then trimmed
means are used for the kmeans family, ignored by all other
families.
Function or name of function to obtain clusters for grouped data, see details below.
Object of class "kcca".
Function kcca returns objects of class "kcca" or
"kccasimple" depending on the value of argument
simple. The simpler objects contain fewer slots and hence are
faster to compute, but contain no auxiliary information used by the
plotting methods. Most plot methods for "kccasimple" objects do
nothing and return a warning. If only centroids, cluster membership or
prediction for new data are of interest, then the simple objects are
sufficient.
Function kccaFamily() currently has the following predefined
families (distance / centroid):
Euclidean distance / mean
Manhattan distance / median
angle between observation and centroid / standardized mean
Jaccard distance / numeric optimization
Jaccard distance / mean
See Leisch (2006) for details on all combinations.
If group is not NULL, then observations from the same
group are restricted to belong to the same cluster (must-link
constraint) or different clusters (cannot-link constraint) during the
fitting process. If groupFun = "minSumClusters", then all group
members are
assign to the cluster where the center has minimal average distance to
the group members. If groupFun = "majorityClusters", then all
group members are assigned to the cluster the majority would belong to
without a constraint.
groupFun = "differentClusters" implements a cannot-link
constraint, i.e., members of one group are not allowed to belong to
the same cluster. The optimal allocation for each group is found by
solving a linear sum assignment problem using
solve_LSAP. Obviously the group sizes must be smaller
than the number of clusters in this case.
Ties are broken at random in all cases.
Note that at the moment not all methods for fitted
"kcca" objects respect the grouping information, most
importantly the plot method when a data argument is specified.
See the paper A Toolbox for K-Centroids Cluster Analysis referenced below for details.
Friedrich Leisch. A Toolbox for K-Centroids Cluster Analysis. Computational Statistics and Data Analysis, 51 (2), 526--544, 2006.
Friedrich Leisch and Bettina Gruen. Extending standard cluster algorithms to allow for group constraints. In Alfredo Rizzi and Maurizio Vichi, editors, Compstat 2006-Proceedings in Computational Statistics, pages 885-892. Physica Verlag, Heidelberg, Germany, 2006.
# NOT RUN {
data("Nclus")
plot(Nclus)
## try kmeans
cl1 <- kcca(Nclus, k=4)
cl1
image(cl1)
points(Nclus)
## A barplot of the centroids
barplot(cl1)
## now use k-medians and kmeans++ initialization, cluster centroids
## should be similar...
cl2 <- kcca(Nclus, k=4, family=kccaFamily("kmedians"),
control=list(initcent="kmeanspp"))
cl2
## ... but the boundaries of the partitions have a different shape
image(cl2)
points(Nclus)
# }
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