Logo Oapen
  • Search
  • Join
    • Deposit
    • For Librarians
    • For Publishers
    • For Researchers
    • Funders
    • Resources
    • OAPEN
    • For Librarians
    • For Publishers
    • For Researchers
    • Funders
    • Resources
    • OAPEN
    View Item 
    •   OAPEN Home
    • View Item
    •   OAPEN Home
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Classification and Data Science in the Digital Age

    Thumbnail
    Download PDF Viewer
    Web Shop
    Contributor(s)
    Brito, Paula (editor)
    Dias, José G. (editor)
    Lausen, Berthold (editor)
    Montanari, Angela (editor)
    Nugent, Rebecca (editor)
    Language
    English
    Show full item record
    Abstract
    The contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education. The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19–23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification.
    URI
    https://library.oapen.org/handle/20.500.12657/86098
    Keywords
    Classification; Data Science; Clustering; Statistical Learning; Machine Learning; Data Analysis; Mutlivariate Analysis; Statistical Inference; Dimension Reduction; Functional Data Analysis; Time Series Analysis; Network Analysis
    DOI
    10.1007/978-3-031-09034-9
    ISBN
    9783031090349, 9783031090332, 9783031090349
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2023
    Imprint
    Springer International Publishing
    Series
    Studies in Classification, Data Analysis, and Knowledge Organization,
    Classification
    Algorithms & data structures
    Artificial intelligence
    Mathematical & statistical software
    Machine learning
    Data mining
    Probability & statistics
    Pages
    416
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

    Browse

    All of OAPENSubjectsPublishersLanguagesCollections

    My Account

    LoginRegister

    Export

    Repository metadata
    Logo Oapen
    • For Librarians
    • For Publishers
    • For Researchers
    • Funders
    • Resources
    • OAPEN

    Newsletter

    • Subscribe to our newsletter
    • view our news archive

    Follow us on

    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

    OAPEN is based in the Netherlands, with its registered office in the National Library in The Hague.

    Director: Niels Stern

    Address:
    OAPEN Foundation
    Prins Willem-Alexanderhof 5
    2595 BE The Hague
    Postal address:
    OAPEN Foundation
    P.O. Box 90407
    2509 LK The Hague

    Websites:
    OAPEN Home: www.oapen.org
    OAPEN Library: library.oapen.org
    DOAB: www.doabooks.org

     

     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Differen formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    A logged-in user can export up to 15000 items. If you're not logged in, you can export no more than 500 items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.