Knowledge discovery in databases: an attribute-oriented rough set approach
Knowledge discovery in databases: an attribute-oriented rough set approach
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Application run time estimation: a quality of service metric for web-based data mining services
Proceedings of the 2002 ACM symposium on Applied computing
Predicting Queue Times on Space-Sharing Parallel Computers
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Using Run-Time Predictions to Estimate Queue Wait Times and Improve Scheduler Performance
IPPS/SPDP '99/JSSPP '99 Proceedings of the Job Scheduling Strategies for Parallel Processing
Prototype selection algorithms for distributed learning
Pattern Recognition
Cluster integration for the cluster-based instance selection
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
Distributed learning with data reduction
Transactions on computational collective intelligence IV
A new cluster-based instance selection algorithm
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
An agent based privacy preserving mining for distributed databases
CIS'04 Proceedings of the First international conference on Computational and Information Science
Performance evaluation of an agent based distributed data mining system
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
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Distributed Data Mining (DDM) is the process of mining distributed and heterogeneous datasets. DDM is widely seen as a means of addressing the scalability issue of mining large data sets. Consequently, there is an emerging focus on optimisation of the DDM process. In this paper we present cost formulae for estimating the communication and computation time for different distributed data mining scenarios.