Identifying Concept-drift in Twitter Streams-Reference-Cited by-同舟云学术

Identifying Concept-drift in Twitter Streams

Published:2015 Issue: Volume:45 Page:86-94
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Lifna C.S.,Vijayalakshmi M.

Publisher

Elsevier BV

Subject

General Engineering

Reference16 articles.

1. Van der Wang, Shenghui, Stefan Schlobach, and Michel Klein. “Concept drift and how to identify it.” Web Semantics: Science, Services andAgents on the World Wide Web Vol. 9 Issue.3 (September 2011) Pg. 247-265.

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3. Wang, Canhui, et al. “Automatic online news topic ranking using media focus and user attention based on aging theory.” Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 2008.

4. Aiello, Luca Maria, et al. “Sensing trending topics in Twitter.” IEEE Trans. Multimedia Vol. 15, Issue. 6, Pages. 1268-1282 (2013). DOI : 10.1109/TMM. 2013.2265080.

5. Jöran Beel and Bela Gipp. Google Scholar‘s Ranking Algorithm: An Introductory Overview. Proceedings of the 12th International Conference on Scientometrics and Informetrics (ISSI’09), volume 1, pages 230-241, Rio de Janeiro (Brazil), July 2009.

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