Abstract
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.
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Parvin, H., Minaei-Bidgoli, B. (2011). A New Space Defined by Ant Colony Algorithm to Partition Data. In: Bouchachia, A. (eds) Adaptive and Intelligent Systems. ICAIS 2011. Lecture Notes in Computer Science(), vol 6943. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23857-4_28
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DOI: https://doi.org/10.1007/978-3-642-23857-4_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23856-7
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