kcca {flexclust} | R Documentation |
Perform k-centroids clustering on a data matrix.
kcca(x, k, family=kccaFamily("kmeans"), weights=NULL, group=NULL, control=NULL, simple=FALSE) kccaFamily(which=NULL, dist=NULL, cent=NULL, name=which, similarity=FALSE, preproc = NULL, trim=0, groupFun = "minSumClusters") ## S4 method for signature 'kccasimple': summary(object)
x |
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). |
k |
Either the number of clusters or a set of initial
(distinct) cluster centroids. If a number, a random set of (distinct)
rows in x is chosen as the initial centroids. |
family |
Object of class kccaFamily . |
weights |
An optional vector of weights to be used in the clustering process, cannot be combined with all families. |
group |
An optional grouping vector for the data, see details below. |
control |
An object of class flexclustControl . |
simple |
Return an object of class kccasimple ? |
which |
One of "kmeans" , "kmedians" ,
"angle" , "jaccard" , or "ejaccard" . |
name |
Optional long name for family, used only for show methods. |
dist |
A function for distance or similarity computation, ignored
if which is specified. |
cent |
A function for centroid computation, ignored
if which is specified. |
similarity |
Logical, if TRUE then dist is
interpreted as a similarity instead of a distance measure, ignored
if which is specified. |
preproc |
Function for data preprocessing. |
trim |
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. |
groupFun |
Function or name of function to obtain clusters for grouped data, currently an experimental feature. |
object |
Object of class "kcca" . |
If group
is not NULL
, then observations from the same
group are restricted to belong to the same cluster during the fitting
process. The cluster for each group is determined by taking the
cluster in which the majority of the group members belong to, ties are
broken at random. 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.
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. All 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.
Friedrich Leisch
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, cluster centroids should be similar ... cl2 = kcca(Nclus, k=4, family=kccaFamily("kmedians")) cl2 ## ... but the boundaries of the partitions have a different shape image(cl2) points(Nclus)