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710	2		_aSpringerLink (Online service) _9106996
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008			181213s2019 gw a o |||| 0|eng d
020			_a9783030018726
024	7		_a10.1007/978-3-030-01872-6 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQA76.9.D343 _b2019 EB
245	0	0	_aLinking and Mining Heterogeneous and Multi-view Data _cedited by Deepak P, Anna Jurek-Loughrey.
264		1	_aCham _bImprint: Springer _c2019
300			_a1 recurso en línea (VIII, 343 páginas) _b66 ilustraciones, 52 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
490	0		_aUnsupervised and Semi-Supervised Learning _x2522-848X
505	0		_aChapter 1. Multi-view Data Completion -- Chapter 2. Multi-view Clustering -- Chapter 3. Semi-supervised and Unsupervised Approaches to Record Pairs Classification in Multi-source Data Linkage -- Chapter 4. A Review of Unsupervised and Semi-Supervised Blocking Methods for Record Linkage -- Chapter 5. Traffic Sensing & Assessing in Digital Transportation Systems -- Chapter 6. How did the discussion go: Discourse act classification in social media conversations -- Chapter 7. Entity Linking in Enterprise Search: Combining Textual and Structural Information -- Chapter 8. Clustering Multi-view Data Using Non-negative Matrix Factorization and Manifold Learning for Effective Understanding: A Survey Paper -- Chapter 9. Leveraging Heterogeneous Data for Fake News Detection -- Chapter 10. On the Evaluation of Community Detection Algorithms on Heterogeneous Social Media Data -- Chapter 11. General Framework for Multi-View Metric Learning -- Chapter 12. Learning from imbalanced datasets with cross-view cooperation-based ensemble methods.
520	3		_aThis book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of growth of data vastly outpacing any chance of labeling them manually. The book looks at the underlying algorithms and technologies that facilitate the area within big data analytics, it covers their applications across domains such as smarter transportation, social media, fake news detection and enterprise search among others. This book enables readers to understand a spectrum of advances in this emerging area, and it will hopefully empower them to leverage and develop methods in multi-source data fusion and analytics with applications to a variety of scenarios. Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion; Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, smarter transportation and social media, among others; Provides a high-level overview of advances in this emerging field and empowers the reader to explore novel applications and methodologies that would enrich the field. .
988			_aPrimersemestre_2019_Engineering
650		7	_2embne _aData mining _9162648
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aJurek-Loughrey, Anna. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aP, Deepak. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030018719
776	0	8	_iPrinted edition: _z9783030018733
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-01872-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b10/2019 _ek _zSI