cluster (version 1.4-1)

clara.object: Clustering Large Applications (CLARA) Object

Description

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

Value

• 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.
• 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 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.
• 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 belongs, as well as the neighbor cluster of i (the cluster, not containing i, for which the average dissimilarity between its observations 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.
• data
  a matrix containing the original or standardized measurements, depending on the stand option of the function clara.

Methods, Inheritance

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.

See Also

clara, dissimilarity.object, partition.object, plot.partition.