Novel and efficient clustering algorithm using structured query language

Clustering becomes an indispensable requirement while dealing with immense volume of data. Since Database Management tools does not provide an inbuilt mechanism to cluster datasets of higher magnitude it inevitably requires an external module to perform the same. This external module should be devised specifically to deal with the data extracted from the data source. There exists myriad of techniques for clustering and the conventional approaches deficits like excessive time and computational complexity. Computational complexity becomes a factor of consideration when data is extracted voluminously and the process of clustering and filtration is performed as a subsequent separate operation. The concept proposed is crafted with an object of eliminating the inherent redundancies in the accustomed practice. The proposed solution relies on exploiting the processing power of the Database Management tool by streamlining the SQL used for data extraction. And hence a procedure is formulated that can combine data retrieval and clustering to one single operation and leave it to DBMS without letting it to dissipate to the adjoining tiers. © 2008 IEEE.