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Hybrid Biclustering Algorithms for Data Mining

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Applications of Evolutionary Computation (EvoApplications 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9597))

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Abstract

Hybrid methods are a branch of biclustering algorithms that emerge from combining selected aspects of pre-existing approaches. The syncretic nature of their construction enriches the existing methods providing them with new properties. In this paper the concept of hybrid biclustering algorithms is explained. A representative hybrid biclustering algorithm, inspired by neural networks and associative artificial intelligence, is introduced and the results of its application to microarray data are presented. Finally, the scope and application potential for hybrid biclustering algorithms is discussed.

P. Orzechowski—AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatics and Bioengineering.

K. Boryczko—AGH University of Science and Technology, Faculty of Computer Science, Electronics and Telecommunications, Department of Computer Science.

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Acknowledgements

This research was funded by the Polish National Science Center (NCN), grant No. 2013/11/N/ST6/03204. This research was supported in part by PL-Grid Infrastructure.

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Authors and Affiliations

  1. Department of Automatics and Bioengineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Cracow, Poland

    Patryk Orzechowski & Krzysztof Boryczko

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  1. Patryk Orzechowski
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  2. Krzysztof Boryczko
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Correspondence to Patryk Orzechowski .

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Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

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Orzechowski, P., Boryczko, K. (2016). Hybrid Biclustering Algorithms for Data Mining. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_11

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  • DOI: https://doi.org/10.1007/978-3-319-31204-0_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31203-3

  • Online ISBN: 978-3-319-31204-0

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