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.
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Kiselev, I., Alhajj, R. (2009). A Multiagent Approach to Adaptive Continuous Analysis of Streaming Data in Complex Uncertain Environments. In: Cao, L. (eds) Data Mining and Multi-agent Integration. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0522-2_14
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DOI: https://doi.org/10.1007/978-1-4419-0522-2_14
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