KmeansParam-class | R Documentation |
Run the base kmeans
function with the specified number of centers within clusterRows
.
KmeansParam(centers, iter.max = NULL, nstart = NULL, algorithm = NULL)
## S4 method for signature 'ANY,KmeansParam'
clusterRows(x, BLUSPARAM, full = FALSE)
centers |
An integer scalar specifying the number of centers. Alternatively, a function that takes the number of observations and returns the number of centers. |
iter.max , nstart , algorithm |
Further arguments to pass to |
x |
A numeric matrix-like object where rows represent observations and columns represent variables. |
BLUSPARAM |
A KmeansParam object. |
full |
Logical scalar indicating whether the full k-means statistics should be returned. |
This class usually requires the user to specify the number of clusters beforehand. However, we can also allow the number of clusters to vary as a function of the number of observations. The latter is occasionally useful, e.g., to allow the clustering to automatically become more granular for large datasets.
To modify an existing KmeansParam object x
,
users can simply call x[[i]]
or x[[i]] <- value
where i
is any argument used in the constructor.
The KmeansParam
constructor will return a KmeansParam object with the specified parameters.
The clusterRows
method will return a factor of length equal to nrow(x)
containing the cluster assignments.
If full=TRUE
, a list is returned with clusters
(the factor, as above) and objects
(a list containing kmeans
, the direct output of kmeans
).
Aaron Lun
kmeans
, which actually does all the heavy lifting.
MbkmeansParam, for a faster but more approximate version of the k-means algorithm.
clusterRows(iris[,1:4], KmeansParam(centers=4))
clusterRows(iris[,1:4], KmeansParam(centers=4, algorithm="Lloyd"))
clusterRows(iris[,1:4], KmeansParam(centers=sqrt))
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