A Multi-Agent Architecture for Distributed Data Mining Systems
The agent-based approach is appealing to Distributed Data Mining (DDM) systems since the concept of agency offers some relevant features, such as scalability, flexibility, robustness, and modularity. This paper investigates whether and how the multi-agent system metaphor might be used for Distributed Data Mining Systems. We proposed and implemented a multi-agent architecture called SeAMS, which is capable of mining patterns efficiently using the DPDTS algorithm. The system was developed in JADE and designed to be easily extensible and protect any local datasets' privacy. Results show that the agentbased approach was able to identify patterns efficiently using three different popular scientific datasets in a distributed time series: Sunspot, Power, and TEK.
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