Papyrus: a system for data mining over local and wide area clusters and super-clusters
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Information Retrieval
Desiderata for agent argumentation protocols
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Argumentation in artificial intelligence
Artificial Intelligence
Agent-Based Non-distributed and Distributed Clustering
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
EMADS: An extendible multi-agent data miner
Knowledge-Based Systems
Best clustering configuration metrics: towards multiagent based clustering
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
A multi-agent based approach to clustering: harnessing the power of agents
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
A framework for Multi-Agent Based Clustering
Autonomous Agents and Multi-Agent Systems
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A framework for Multi Agent Data Mining (MADM) is described. The framework comprises a collection of agents cooperating to address given data mining tasks. The fundamental concept underpinning the framework is that it should support generic data mining. The vision is that of a system that grows in an organic manner. The central issue to facilitating this growth is the communication medium required to support agent interaction. This issue is partly addressed by the nature of the proposed architecture and partly through an extendable ontology; both are described. The advantages offered by the framework are illustrated in this paper by considering a clustering application. The motivation for the latter is that no "best" clustering algorithm has been identified, and consequently an agent-based approach can be adopted to identify "best" clusters. The application serves to demonstrates the full potential of MADM.