Abstract
Bacterial colonies perform a cooperative and distributed exploration of the environmental resources. This paper describes how bacterial colony networks and their skills to search resources can be used as tools for mining association rules in static and stream data. The proposed algorithm is designed to maintain diverse solutions to the problems at hand, and its performance is compared to another well-known bacterial algorithm in both static and stream datasets.
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Acknowledgments
The authors thank CAPES, CNPq, Fapesp, and Mackpesquisa for the financial support. The authors also acknowledge the support of Intel for the Natural Computing and Machine Learning Laboratory as an Intel Center of Excellence in Machine Learning.
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da Cunha, D.S., Xavier, R.S., Ferrari, D.G., de Castro, L.N. (2018). Bacterial Colony Algorithms Applied to Association Rule Mining in Static Data and Streams. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Communications in Computer and Information Science, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-94779-2_45
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DOI: https://doi.org/10.1007/978-3-319-94779-2_45
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