000			01713nam a2200253 4500
005			20250714133157.0
008			250714b2020|||||||| |||| 00| 0 eng d
020			_a9781119695103
041			_aEnglish
082			_a005.74
100			_aDiday, Edwin _eEditor _96456
100			_aGuan, Rong _eCo-Editor _96457
100			_aSaporta, Gilbert _eCo-Editor _96458
100			_aWang, Huiwen _eCo-Editor _96459
245			_aAdvances in data science: symbolic, complex, and network data
260			_aNew Jersey: _bWiley Data and Cybersecurity, _c2020.
300			_axi, 233p.
500			_aData 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.
650			_aExplanatory Tools for Machine Learning in the Symbolic Data Analysis Framework _96460
650			_aIncremental Calculation Framework for Complex Data _96461
650			_aRecommender Systems and Attributed Networks _96462
856			_uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=9820889
942			_cEB
999			_c1605 _d1605