Clustering algorithms for Very Large Data Bases (VLDB) are observed in application with image and video processing. Such a specific case requires initial data presentation as multidimen-sional vectors. That is why matrix modifications of traditional k-medoids, Partitioning Around Medoids, Clustering LARge Applications and CLARA based on RANdomized Search methods are proposed. Benefits and drawbacks of them all are examined.
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