Partitioning Methods in Large Database. A typical k medoids-partitioning algorithm like PAM works effectively for small data set, but does not scale well for large data sets. To deal with large data sets, a sampling-based method called Clustering LARge Application (CLARA) can be used. The effectiveness of CLARA depends on sample size to notice that PAM searches for the best k-medoids among given data set, whereas CLARA searches for the best k-medoids among the selected sample of the data set. A k-medoids type algorithm called Clustering Large Application based upon Randomized Search (CLARANS) was proposed that combines the sampling technique with PAM. However unlike, CLARA, CLARANS does not confine itself to any sample at any given time. While CLARA has fixed sample at each stage of search, CLARANS draws sample with some randomness in each step of search, it has been experimentally shown to be more effective than PAM and CLARA. It can be used to find the most “Natural” number of clusters using a silhouette coefficient – a property of an object that specifies how much the object truly belongs to the cluster.
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