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A Bacterial Colony Algorithm for Association Rule Mining

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Intelligent Data Engineering and Automated Learning – IDEAL 2015 (IDEAL 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9375))

Abstract

Bacterial colonies perform a cooperative distributed exploration of the environment. This paper describes bacterial colony networks and their skills to explore resources as a tool for mining association rules in databases. The proposed algorithm is designed to maintain diverse solutions to the problem at hand, and its performance is compared to other well-known bio-inspired algorithms, including a genetic and an immune algorithm (CLONALG) and, also, to Apriori over some benchmarks from the literature.

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Acknowledgement

The authors thank CAPES, Fapesp, CNPq and MackPesquisa for the financial support.

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Authors and Affiliations

  1. Natural Computing Laboratory - LCoN, Mackenzie Presbyterian University, São Paulo, Brazil

    Danilo Souza da Cunha, Rafael Silveira Xavier & Leandro Nunes de Castro

Authors
  1. Danilo Souza da Cunha
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  2. Rafael Silveira Xavier
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  3. Leandro Nunes de Castro
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Corresponding author

Correspondence to Danilo Souza da Cunha .

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, Wroclaw, Poland

    Konrad Jackowski

  2. Department of Systems, Wroclaw University of Technology, Wroclaw, Poland

    Robert Burduk

  3. Wroclaw Univ of Tech, Wroclaw, Poland

    Krzysztof Walkowiak

  4. Wroclaw University of Technology, Faculty of Electronics, Wroclaw, Poland

    Michal Wozniak

  5. School of Electrical & Electronic E, University of Manchester, Manchester, United Kingdom

    Hujun Yin

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da Cunha, D.S., Xavier, R.S., de Castro, L.N. (2015). A Bacterial Colony Algorithm for Association Rule Mining. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-24834-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24833-2

  • Online ISBN: 978-3-319-24834-9

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