ABSTRACT: The data mining task of online unsupervised learning of streaming data continually arriving at the system in complex dynamic environments under conditions of uncertainty is an
NP-hard optimization problem for general metric spaces and is computationally intractable for real-world problems of practical
interest. The primary contribution of this work is a multi-agent method for continuous agglomerative hierarchical clustering
of streaming data, and a knowledge-based selforganizing competitive multi-agent system for implementing it. The reported experimental results demonstrate the applicability and efficiency of the implemented adaptive
multi-agent learning system for continuous online clustering of both synthetic datasets and datasets from the following real-world
domains: the RoboCup Soccer competition, and gene expression datasets from a bioinformatics test bed.
12/2008: pages 201-218;
Proceedings of the 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Sydney, NSW, Australia, December 9-12, 2008; 01/2008
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17, 2008; 01/2008
ABSTRACT: The task of continuous online unsupervised learning of streaming data in complex dynamic environments under conditions of uncertainty is an NP-hard optimization problem for general metric spaces. This paper describes a computationally efficient adaptive multi-agent approach to continuous online clustering of streaming data, which is originally sensitive to environmental variations and provides a fast dynamic response with event-driven incremental improvement of optimization results, trading-off operating time and result quality. Experimental results demonstrate the strong performance of the implemented multi-agent learning system for continuous online optimization of both synthetic datasets and datasets from the RoboCup Soccer and Rescue domains.