Edwin (Hrsg.) Diday

Advances in Data Science

Symbolic, Complex, and Network Data

Ebook (EPUB Format)

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 a… Mehr

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Produktdetails

Weitere Autoren: Wang, Huiwen (Hrsg.) / Saporta, Gilbert (Hrsg.) / Guan, Rong (Hrsg.)

ISBN: 978-1-119-69496-0
EAN: 9781119694960
Produktnummer: 33393128
Verlag: Wiley
Sprache: Englisch
Erscheinungsjahr: 2020
Seitenangabe: 258 S.
Plattform: EPUB
Masse: 17'477 KB

Über den Autor

Edwin Diday is Emeritus Professor at Paris-Dauphine University-PSL. He helped to introduce the symbolic data analysis paradigm and the dynamic clustering method (opening the path to local models), as well as pyramidal clustering for spatial representation of overlapping clusters. Rong Guan is Associate Professor at the School of Statistics and Mathematics, Central University of Finance and Economics, Beijing. Her research covers complex and symbolic data analysis and financial distress diagnosis. Gilbert Saporta is Emeritus Professor at Conservatoire National des Arts et Metiers, France. His current research focuses on functional data analysis and clusterwise and sparse methods. He is Honorary President of the French Statistical Society. Huiwen Wang is Professor at the School of Economics and Management, Beihang University, Beijing. Her research covers dimension reduction, PLS regression, symbolic data analysis, compositional data analysis, functional data analysis and statistical modeling methods for mixed data.