Clustering Large Applications (CLARA) Object

clara.object {cluster}

R Documentation

The objects of class "clara" represent a partitioning of a large dataset into clusters and are typically returned from clara.

A legitimate clara object is a list with the following components:

`sample`	labels or case numbers of the observations in the best sample, that is, the sample used by the `clara` algorithm for the final partition.
`medoids`	the medoids or representative objects of the clusters. It is a matrix with in each row the coordinates of one medoid. Possibly `NULL`, namely when the object resulted from `clara(*, medoids.x=FALSE)`. Use the following `i.med` in that case.
`i.med`	the indices of the `medoids` above: `medoids <- x[i.med,]` where `x` is the original data matrix in `clara(x,*)`.
`clustering`	the clustering vector, see `partition.object`.
`objective`	the objective function for the final clustering of the entire dataset.
`clusinfo`	matrix, each row gives numerical information for one cluster. These are the cardinality of the cluster (number of observations), the maximal and average dissimilarity between the observations in the cluster and the cluster's medoid. The last column is the maximal dissimilarity between the observations in the cluster and the cluster's medoid, divided by the minimal dissimilarity between the cluster's medoid and the medoid of any other cluster. If this ratio is small, the cluster is well-separated from the other clusters.
`diss`	dissimilarity (maybe NULL), see `partition.object`.
`silinfo`	list with silhouette width information for the best sample, see `partition.object`.
`call`	generating call, see `partition.object`.
`data`	matrix, possibibly standardized, or NULL, see `partition.object`.

The "clara" class has methods for the following generic functions: print, summary.

The class "clara" inherits from "partition". Therefore, the generic functions plot and clusplot can be used on a clara object.