Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Mobile Active Object for Highly Dynamic Distributed Computing
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Developing multiagent systems: The Gaia methodology
ACM Transactions on Software Engineering and Methodology (TOSEM)
Multi-coordination of mobile agents: a model and a component-based architecture
Proceedings of the 2005 ACM symposium on Applied computing
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Load Balancing for the Distributed Mining of Molecular Structures
IEEE Transactions on Parallel and Distributed Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Agent-based distributed data mining: the KDEC scheme
Intelligent information agents
A Generic and Extendible Multi-Agent Data Mining Framework
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.