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Local Models of Interaction on Collinear Patterns

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Book cover Computational Collective Intelligence (ICCCI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11683))

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Abstract

Data mining algorithms can be used for discovering collinear patterns in data sets composed of a large number of multidimensional feature vectors. Collinear (flat) pattern is observed in data sets when many feature vectors are located on a plane in a feature space. Models of linear interactions between multiple features (genes) can be designed based on collinear patterns. Minimization of the convex and piecewise linear (CPL) criterion functions allows for efficient discovering of flat patterns even in cases of large data sets.

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Acknowledgments

The presented study was supported by the grant S/WI/2/2019 from Bialystok University of Technology and funded from the resources for research by Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Faculty of Computer Science, Białystok University of Technology, Białystok, Poland

    Leon Bobrowski

  2. Institute of Biocybernetics and Biomedical Engineering, PAS, Warsaw, Poland

    Leon Bobrowski

Authors
  1. Leon Bobrowski
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Corresponding author

Correspondence to Leon Bobrowski .

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Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. University of Pau and Pays de l’Adour, Pau, France

    Richard Chbeir

  3. University of Pau and Pays de l’Adour, Pau, France

    Ernesto Exposito

  4. University of Pau and Pays de l’Adour, Pau, France

    Philippe Aniorté

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Bobrowski, L. (2019). Local Models of Interaction on Collinear Patterns. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_21

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  • DOI: https://doi.org/10.1007/978-3-030-28377-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28376-6

  • Online ISBN: 978-3-030-28377-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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