IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
An Agent-Based Hierarchical Clustering Approach for E-commerce Environments
EC-WEB '02 Proceedings of the Third International Conference on E-Commerce and Web Technologies
Towards a Theory of Cooperative Problem Solving
MAAMAW '94 Proceedings of the 6th European Workshop on Modelling Autonomous Agents: Distributed Software Agents and Applications
A method for decentralized clustering in large multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Post-mining: maintenance of association rules by wieghting
Information Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient agent-based cluster ensembles
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Agent clustering based on semantic negotiation
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Emergent Intelligence of Networked Agents
Emergent Intelligence of Networked Agents
On simultaneous selection of prototypes and features in large data
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
A framework for Multi-Agent Based Clustering
Autonomous Agents and Multi-Agent Systems
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Multiagent Systems consist of multiple computing elements called agents, which in order to achieve a given objective, can act on their own, react to the inputs, pro-act and cooperate. Data Mining deals with large data. Large data clustering is a data mining activity where in efficient clustering algorithms select a subset of original dataset as representative patterns. In the current work we propose a multi-agent based clustering scheme that combines multiple agents, each capable of generating a set of prototypes using an independent prototype selection algorithm. Each prototype set is used to predict the labels of unseen data. The results of these agents are combined by another agent resulting in a high classification accuracy. Such a scheme is of high practical utility in dealing with large datasets.