The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Requirements for clustering data streams
ACM SIGKDD Explorations Newsletter
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic k-means: a clustering technique for moving object trajectories
International Journal of Intelligent Information and Database Systems
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In this paper, a multiagent algorithm for dynamic clustering is presented. This kind of clustering is intended to manage mobile data and so, to be able to continuously adapt the built clusters. First of all, potential applications of this algorithm are presented. Then the specific constraints for this kind of clustering are studied. A multiagent architecture satisfying these constraints is described. It combines an ants algorithm with a cluster agents layer which are executed simultaneously. Finally, the first experimental results of our work are presented.