Large-Scale Parallel Data Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
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
Group formation among peer-to-peer agents: learning group characteristics
AP2PC'03 Proceedings of the Second international conference on Agents and Peer-to-Peer Computing
On Decentralised Clustering in self-monitoring networks
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
On convergence of dynamic cluster formation in multi-agent networks
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Symbiotic sensor networks in complex underwater terrains: a simulation framework
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Predicting cluster formation in decentralized sensor grids
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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A Decentralised Adaptive Clustering (DAC) algorithm for self-monitoring impact sensing networks is presented within the context of CSIRO-NASA Ageless Aero-space Vehicle project. DAC algorithm is contrasted with a Fixed-order Centralised Adaptive Clustering (FCAC) algorithm, developed to evaluate the comparative performance. A number of simulation experiments is described, with a focus on the scalability and convergence rate of the clustering algorithm. Results show that DAC algorithm scales well with increasing network and data sizes and is robust to dynamics of the sensor-data flux.