Scalable biclustering — the future of big data exploration?

Biclustering is a technique of discovering local similarities within data. For many years the complexity of the methods and parallelization issues limited its application to big data problems. With the development of novel scalable methods, biclustering has finally started to close this gap. In this...

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Detalles Bibliográficos
Autores principales: Orzechowski, Patryk, Boryczko, Krzysztof, Moore, Jason H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Commentary
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6598466/
https://www.ncbi.nlm.nih.gov/pubmed/31251324
http://dx.doi.org/10.1093/gigascience/giz078
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author Orzechowski, Patryk
Boryczko, Krzysztof
Moore, Jason H
author_facet Orzechowski, Patryk
Boryczko, Krzysztof
Moore, Jason H
author_sort Orzechowski, Patryk
collection PubMed
description Biclustering is a technique of discovering local similarities within data. For many years the complexity of the methods and parallelization issues limited its application to big data problems. With the development of novel scalable methods, biclustering has finally started to close this gap. In this paper we discuss the caveats of biclustering and present its current challenges and guidelines for practitioners. We also try to explain why biclustering may soon become one of the standards for big data analytics.
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spelling pubmed-65984662019-07-03 Scalable biclustering — the future of big data exploration? Orzechowski, Patryk Boryczko, Krzysztof Moore, Jason H Gigascience Commentary Biclustering is a technique of discovering local similarities within data. For many years the complexity of the methods and parallelization issues limited its application to big data problems. With the development of novel scalable methods, biclustering has finally started to close this gap. In this paper we discuss the caveats of biclustering and present its current challenges and guidelines for practitioners. We also try to explain why biclustering may soon become one of the standards for big data analytics. Oxford University Press 2019-06-28 /pmc/articles/PMC6598466/ /pubmed/31251324 http://dx.doi.org/10.1093/gigascience/giz078 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Commentary
Orzechowski, Patryk
Boryczko, Krzysztof
Moore, Jason H
Scalable biclustering — the future of big data exploration?
title Scalable biclustering — the future of big data exploration?
title_full Scalable biclustering — the future of big data exploration?
title_fullStr Scalable biclustering — the future of big data exploration?
title_full_unstemmed Scalable biclustering — the future of big data exploration?
title_short Scalable biclustering — the future of big data exploration?
title_sort scalable biclustering — the future of big data exploration?
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6598466/
https://www.ncbi.nlm.nih.gov/pubmed/31251324
http://dx.doi.org/10.1093/gigascience/giz078
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