Advances in data science: symbolic, complex, and network data
Material type:
TextLanguage: English Publication details: New Jersey: Wiley Data and Cybersecurity, 2020. Description: xi, 233pISBN: 9781119695103Subject(s): Explanatory Tools for Machine Learning in the Symbolic Data Analysis Framework | Incremental Calculation Framework for Complex Data | Recommender Systems and Attributed NetworksDDC classification: 005.74 Online resources: Click here to access online
| Item type | Current library | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| e-Books | Dr. S. R. Ranganathan Library Ebook (Online Access) | 005.74 (Online Access) (Browse shelf(Opens below)) | Available | EB0033 |
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.