Arguments
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.
clustering
the clustering vector. A vector with length equal to the number of
observations, giving the number of the cluster to which each observation
belongs.
objective
the objective function for the final clustering of the entire dataset.
clusinfo
matrix, each row gives numeric information for one cluster. These are
the cardinality of the cluster (number of observations), and the maximal and
average dissimilarity between the observations in the cluster and the cluster's
medoid. The last column is t
silinfo
list with all information necessary to construct a silhouette plot of the
clustering of the best sample. This list is only available when 1 < k < n.
The first component is a matrix, with for each observation i in the best
sample, the cluster to which i b
diss
an object of class "dissimilarity", representing the total dissimilarity
matrix of the dataset.
data
a matrix containing the original or standardized measurements, depending
on the stand option of the function clara.
GENERATION
This class of objects is returned from clara.METHODS
The "clara" class has methods for the following generic functions:
print, summary.INHERITANCE
The class "clara" inherits from "partition".
Therefore, the generic functions plot and clusplot can be used on a
clara object.STRUCTURE
A legitimate clara object is a list with the following components: