Clustering in LARge Applications is called as CLARA. The efficiency of CLARA depends upon the size of the representative data set.
CLARA does not work properly if any representative data set from the selected representative data sets does not find best k-medoids.
To recover this drawback a new algorithm, Clustering Large Applications based upon RANdomized search (CLARANS) is introduced.
The CLARANS works like CLARA, the only difference between CLARA and CLARANS is the clustering process that is done after selecting the representative data sets.