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020 _a9783030018726
_9978-3-030-01872-6
024 7 _a10.1007/978-3-030-01872-6
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
_2thema
082 0 4 _a621.382
_223
245 1 0 _aLinking and Mining Heterogeneous and Multi-view Data
_h[electronic resource] /
_cedited by Deepak P, Anna Jurek-Loughrey.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aVIII, 343 p. 66 illus., 52 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUnsupervised and Semi-Supervised Learning,
_x2522-8498
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 _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. .
650 0 _aTelecommunication.
_910437
650 0 _aSignal processing.
_94052
650 0 _aPattern recognition systems.
_93953
650 0 _aArtificial intelligence.
_93407
650 0 _aData mining.
_93907
650 1 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aData Mining and Knowledge Discovery.
_939180
700 1 _aP, Deepak.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_939181
700 1 _aJurek-Loughrey, Anna.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_939182
710 2 _aSpringerLink (Online service)
_939183
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030018719
776 0 8 _iPrinted edition:
_z9783030018733
830 0 _aUnsupervised and Semi-Supervised Learning,
_x2522-8498
_939184
856 4 0 _uhttps://doi.org/10.1007/978-3-030-01872-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c76505
_d76505