Future Generation Computer Systems - Special issue on metacomputing
MPICH-G2: a Grid-enabled implementation of the Message Passing Interface
Journal of Parallel and Distributed Computing - Special issue on computational grids
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Fast condensed nearest neighbor rule
ICML '05 Proceedings of the 22nd international conference on Machine learning
Distributed Nearest Neighbor-Based Condensation of Very Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
Protein data condensation for effective quaternary structure classification
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Fast minimization of structural risk by nearest neighbor rule
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Grid computing provides services to the users to discover, transfer, and manipulate large datasets distributed in different locations. Classifying large datasets without using a centralized approach is a key problem in this kind of architectures and, for instance, it is essential for the ever growing datasets bioinformatic scientists face. To this aim, Grid-FCNN, a grid-enabled architecture for classifying huge data set using the nearest neighbor rule is presented in this paper. In order to cope with the communication overhead typical of distributed environments and to reduce memory requirements, two different strategies are presented, namely Grid-FCNN1 and Grid-FCNN2, and their performances in grid environments is analyzed. An analysis of the experimental results, performed on both synthetic and real very large datasets, revealed that these techniques are adapt to be used in a Grid. Furthermore, it is illustrated how the Grid-based algorithm can be applicable in a real bioinformatics scenario.