PamParam-class {bluster} | R Documentation |
Partition observations into k-medoids as a more robust version of k-means.
PamParam( centers, metric = NULL, medoids = NULL, nstart = NULL, stand = NULL, do.swap = NULL, variant = NULL ) ## S4 method for signature 'ANY,PamParam' 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. |
metric, medoids, nstart, stand, do.swap, variant |
Further arguments to pass to |
x |
A numeric matrix-like object where rows represent observations and columns represent variables. |
BLUSPARAM |
A PamParam object. |
full |
Logical scalar indicating whether the full PAM 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 PamParam object x
,
users can simply call x[[i]]
or x[[i]] <- value
where i
is any argument used in the constructor.
The PamParam
constructor will return a PamParam 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 pam
, the direct output of pam
).
Aaron Lun
pam
, which actually does all the heavy lifting.
KmeansParam, for the more commonly used k-means algorithm.
ClaraParam, for a scalable extension to the PAM approach.
clusterRows(iris[,1:4], PamParam(centers=4)) clusterRows(iris[,1:4], PamParam(centers=4, variant="faster", do.swap=FALSE)) clusterRows(iris[,1:4], PamParam(centers=sqrt))