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Advances in Data Science. Symbolic, Complex and Network Data

Abstract : Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.
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Directions of work or proceedings
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Contributor : Gilbert Saporta Connect in order to contact the contributor
Submitted on : Saturday, February 8, 2020 - 7:02:52 PM
Last modification on : Friday, August 5, 2022 - 2:54:00 PM


  • HAL Id : hal-02471591, version 1


Edwin Diday, Rong Guan, Gilbert Saporta, Huiwen Wang. Advances in Data Science. Symbolic, Complex and Network Data. ISTE-WILEY, 2020, Big Data, Artificial Intelligence and Data Analysis, 978-1-786-30576-3. ⟨hal-02471591⟩



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