Clustering Large Applications Object
DESCRIPTION:
These are objects of class "clara" They represent a partitioning of a large dataset into clusters.

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". By that, the generic function plot can be used with a clara object.

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

sample:
labels or case numbers of the objects 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 objects, giving the number of the cluster to which each object 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 objects), and the maximal and average dissimilarity between the objects in the cluster and the cluster's medoid. The last column is the maximal dissimilarity between the objects 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.

silinfo:
list with all information necessary to construct a silhouette plot of the clustering of the best sample. The first component is a matrix, with for each object i in the best sample, the cluster to which i belongs, as well as the neighbor cluster of i (the cluster, not containing i, for which the average dissimilarity between its objects and i is minimal), and the silhouette width of i. The other two components give the average silhouette width per cluster and the average silhouette width for the best sample. See plot.partition for more information.

diss:
an object of class "dissimilarity", representing the total dissimilarity matrix of the dataset.

SEE ALSO:
clara , dissimilarity.object , partition.object , plot.partition .