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
Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Swarm intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Clustering Ensembles Using Ants Algorithm
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
Incremental clustering based on swarm intelligence
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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To reach a robust partition, ensemble-based learning is always a very promising option. There is straightforward way to generate a set of primary partitions that are different from each other, and then to aggregate the partitions via a consensus function to generate the final partition. Another alternative in the ensemble learning is to turn to fusion of different data from originally different sources. In this paper we introduce a new ensemble learning based on the Ant Colony clustering algorithm. Experimental results on some real-world datasets are presented to demonstrate the effectiveness of the proposed method in generating the final partition.