On matrix modification of clarans clustering method in large video surveillance databases
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.