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Big data naturally rescaled

Author

Listed:
  • Stoop, Ruedi
  • Kanders, Karlis
  • Lorimer, Tom
  • Held, Jenny
  • Albert, Carlo

Abstract

We propose that a handle could be put on big data by looking at the systems that actually generate the data, rather than the data itself, realizing that there may be only few generic processes involved in this, each one imprinting its very specific structures in the space of systems, the traces of which translate into feature space. From this, we propose a practical computational clustering approach, optimized for coping with such data, inspired by how the human cortex is known to approach the problem.

Suggested Citation

  • Stoop, Ruedi & Kanders, Karlis & Lorimer, Tom & Held, Jenny & Albert, Carlo, 2016. "Big data naturally rescaled," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 81-90.
    • Handle: RePEc:eee:chsofr:v:90:y:2016:i:c:p:81-90
    DOI: 10.1016/j.chaos.2016.02.035
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    References listed on IDEAS

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    1. Bryan, Jenny, 2004. "Problems in gene clustering based on gene expression data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 44-66, July.
    2. Manfred G Kitzbichler & Marie L Smith & Søren R Christensen & Ed Bullmore, 2009. "Broadband Criticality of Human Brain Network Synchronization," PLOS Computational Biology, Public Library of Science, vol. 5(3), pages 1-13, March.
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    Cited by:

    1. Carbone, Anna & Jensen, Meiko & Sato, Aki-Hiro, 2016. "Challenges in data science: a complex systems perspective," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 1-7.
    2. Lorimer, Tom & Kanders, Karlis & Stoop, Ruedi, 2019. "Natural data structure extracted from neighborhood-similarity graphs," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 326-331.

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