Advances in data science symbolic, complex, and network data
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 req...
| Other Authors: | , , , |
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| Format: | eBook |
| Language: | Inglés |
| Published: |
London, England ; Hoboken, New Jersey :
ISTE
[2020]
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| Edition: | 1st edition |
| Series: | Innovation, entrepreneurship and management series. Big data, artificial intelligence and data analysis set ;
v. 4. |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630782006719 |
| Summary: | 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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| Physical Description: | 1 online resource (253 pages) |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781119695103 9781119695110 9781119694960 |
