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Big data naturally rescaled


Stoop, Ruedi; Kanders, Karlis; Lorimer, Tom; Held, Jenny; Albert, Carlo (2016). Big data naturally rescaled. Chaos, Solitons & Fractals, 90:81-90.

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.

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.

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1 citation in Scopus®
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Additional indexing

Item Type:Journal Article, not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2016
Deposited On:26 Jan 2017 14:02
Last Modified:26 Jan 2017 14:04
Publisher:Elsevier
ISSN:0960-0779
Publisher DOI:https://doi.org/10.1016/j.chaos.2016.02.035

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