GridclassTK: toolkit for grid learning classifier systems
ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
Distributed data mining for e-business
Information Technology and Management
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Distributed data mining (DDM) techniques have become necessary for large and multi-scenario datasets requiring resources, which are heterogeneous and distributed. In this paper, we focused on distributed data mining based in grid. We have discussed and analyzed a new framework based on grid environments to execute new distributed data mining techniques on very large and distributed heterogeneous datasets. The architecture and motivation for the design have been presented. We pointed out that distributed data mining technology can offer a better solution since they are designed to work in a distributed environment by paying careful attention to the computing and communication resources.